Bank / gen5

Viewer
!new Bank('bank9')
!bank9.country := 'Italy'
!bank9.name := 'UniCredit'
!bank9.bic := 'UNCRITMM'

!new Bank('bank10')
!bank10.country := 'South Africa'
!bank10.name := 'First National Bank'
!bank10.bic := 'FIRNZAJJ'

!new Account('account13')
!account13.iban := 'IT6010203030405050678989898'
!account13.balance := 4000

!new Account('account14')
!account14.iban := 'ZA4200005001234567890123'
!account14.balance := 0

!new Account('account15')
!account15.iban := 'IT6010203030405060780878797'
!account15.balance := 2800

!new Person('person13')
!person13.firstName := 'Marco'
!person13.lastName := 'Rossi'
!person13.age := 27

!new Person('person14')
!person14.firstName := 'Thandi'
!person14.lastName := 'Naidoo'
!person14.age := 33

!new Person('person15')
!person15.firstName := 'Elena'
!person15.lastName := 'Bianchi'
!person15.age := 29

!insert (person13, account13) into Ownership
!insert (person14, account14) into Ownership
!insert (person15, account15) into Ownership
!insert (person13, account15) into Ownership

!insert (person13, account13) into Use
!insert (person14, account14) into Use
!insert (person15, account15) into Use
!insert (person15, account13) into Use

!insert (bank9, account13) into AccountOfBanks
!insert (bank10, account14) into AccountOfBanks
!insert (bank9, account15) into AccountOfBanks
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
Given a conceptual model expressed in the UML-based Specification Environment (USE), your task is to generate valid and realistic instances that conform to the provided model. <requirements> - Instances must be syntactically correct according to the USE syntax_reference. - Avoid unnecessary comments and output the instance in plain text (i.e., not markdown). - Make sure instances fulfill all the model's constraints, and that multiplicities, relationships, and attributes are valid and realistic. - Provide multiple instances with diverse data values and structure. </requirements>  <syntax_reference> Here there is a snippet showing how to create objects and set values in the specific .soil language required: -- This is a comment example  -- Primitive data types:     -- Integer i.e. 1, 2, 3, etc.     -- Real i.e. 1.0, 21.89, 322.05556, etc.     -- Boolean i.e. true or false     -- String i.e. 'Hello World'  -- You can create instances with the following syntax: !new <instance type>('<instance name>') -- Example: !new Client('client1') !new Store('store4')  -- You can assign values to attributes for a created instance with the following syntax: !<instance name>.<attribute name> := <value> -- Example for different data types: !client1.clientId := 1 -- For Integer !client1.balance := 1123.45 -- For Real !client1.name := 'John' -- For Strings !store4.available := true -- For Boolean  -- You can create associations between instances with the following syntax: !insert (<instance name1>, <instance name2>) into <association name> -- Example: !insert (client1, store4) into ClientStore  -- Custom data types usage:     -- dataType Location     -- operations     --   Location(x : Real, y : Real)     --   some other operations     -- end -- You can create custom data types by calling the constructor directly; in this case, the constructor of Location requires two arguments: x and y of type Real. So it can be used as follows: !store4.location := Location(14.0, 289.0)  -- Enums usage:     -- enum Type { Clothes, Shoes } -- Can be used as follows: !store4.type := #Clothes </syntax_reference>
Please generate another instance that is structurally and semantically different from the previous ones.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('bank9')
!bank9.country := 'Italy'
!bank9.name := 'UniCredit'
!bank9.bic := 'UNCRITMM'

!new Bank('bank10')
!bank10.country := 'South Africa'
!bank10.name := 'First National Bank'
!bank10.bic := 'FIRNZAJJ'

!new Account('account13')
!account13.iban := 'IT6010203030405050678989898'
!account13.balance := 4000

!new Account('account14')
!account14.iban := 'ZA4200005001234567890123'
!account14.balance := 0

!new Account('account15')
!account15.iban := 'IT6010203030405060780878797'
!account15.balance := 2800

!new Person('person13')
!person13.firstName := 'Marco'
!person13.lastName := 'Rossi'
!person13.age := 27

!new Person('person14')
!person14.firstName := 'Thandi'
!person14.lastName := 'Naidoo'
!person14.age := 33

!new Person('person15')
!person15.firstName := 'Elena'
!person15.lastName := 'Bianchi'
!person15.age := 29

!insert (person13, account13) into Ownership
!insert (person14, account14) into Ownership
!insert (person15, account15) into Ownership
!insert (person13, account15) into Ownership

!insert (person13, account13) into Use
!insert (person14, account14) into Use
!insert (person15, account15) into Use
!insert (person15, account13) into Use

!insert (bank9, account13) into AccountOfBanks
!insert (bank10, account14) into AccountOfBanks
!insert (bank9, account15) into AccountOfBanks
</object_model>
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The object model perfectly aligns with real-world banking semantics. The banks ("UniCredit", "First National Bank") have valid BICs and country locations. The IBANs correctly match their respective bank's country codes ('IT' for Italy, 'ZA' for South Africa). The names of the persons are culturally consistent with their locations, all account owners are adults, balances are non-negative, and joint ownership respects the 1-to-2 owner limit.

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.01
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/40
Multiplicities 0/11
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 92.5%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 8/∞
Attributes 21/21
Relationships 11/∞
Viewer
!new Bank('stateBankOfIndia')
!stateBankOfIndia.country := 'India'
!stateBankOfIndia.name := 'State Bank of India'
!stateBankOfIndia.bic := 'SBININBBXXX'

!new Account('account18_1')
!account18_1.iban := 'IN4321098765432109876543210'
!account18_1.balance := 3000

!new Account('account18_2')
!account18_2.iban := 'IN5432109876543210987654321'
!account18_2.balance := 15000

!new Account('account18_3')
!account18_3.iban := 'IN6543210987654321098765432'
!account18_3.balance := 45000

!new Account('account18_4')
!account18_4.iban := 'IN7654321098765432109876543'
!account18_4.balance := 600

!new Account('account18_5')
!account18_5.iban := 'IN8765432109876543210987654'
!account18_5.balance := 12000

!new Account('account18_6')
!account18_6.iban := 'IN9876543210987654321098765'
!account18_6.balance := 85000

!new Person('rohanSharma')
!rohanSharma.firstName := 'Rohan'
!rohanSharma.lastName := 'Sharma'
!rohanSharma.age := 29

!new Person('priyaKapoor')
!priyaKapoor.firstName := 'Priya'
!priyaKapoor.lastName := 'Kapoor'
!priyaKapoor.age := 35

!new Person('arjunIyer')
!arjunIyer.firstName := 'Arjun'
!arjunIyer.lastName := 'Iyer'
!arjunIyer.age := 42

!new Person('anjaliGupta')
!anjaliGupta.firstName := 'Anjali'
!anjaliGupta.lastName := 'Gupta'
!anjaliGupta.age := 37

!new Person('vivekRao')
!vivekRao.firstName := 'Vivek'
!vivekRao.lastName := 'Rao'
!vivekRao.age := 31

!insert (stateBankOfIndia, account18_1) into AccountOfBanks
!insert (stateBankOfIndia, account18_2) into AccountOfBanks
!insert (stateBankOfIndia, account18_3) into AccountOfBanks
!insert (stateBankOfIndia, account18_4) into AccountOfBanks
!insert (stateBankOfIndia, account18_5) into AccountOfBanks
!insert (stateBankOfIndia, account18_6) into AccountOfBanks

!insert (rohanSharma, account18_1) into Ownership

!insert (priyaKapoor, account18_2) into Ownership
!insert (arjunIyer, account18_2) into Ownership

!insert (anjaliGupta, account18_3) into Ownership
!insert (rohanSharma, account18_3) into Ownership

!insert (vivekRao, account18_4) into Ownership

!insert (priyaKapoor, account18_5) into Ownership

!insert (anjaliGupta, account18_6) into Ownership
!insert (vivekRao, account18_6) into Ownership

!insert (rohanSharma, account18_1) into Use
!insert (priyaKapoor, account18_1) into Use

!insert (arjunIyer, account18_2) into Use
!insert (anjaliGupta, account18_2) into Use

!insert (rohanSharma, account18_3) into Use
!insert (vivekRao, account18_3) into Use

!insert (vivekRao, account18_4) into Use

!insert (priyaKapoor, account18_5) into Use
!insert (anjaliGupta, account18_5) into Use

!insert (anjaliGupta, account18_6) into Use
!insert (rohanSharma, account18_6) into Use
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand.  You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose.  ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association.  ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled).
Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes.
You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created.  Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
Analyze the following UML class diagram:
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
For the same category and model description, generate another instance that is structurally and semantically different from the previous ones
Continue with the following description, creating the instance according to the syntax example and this specification: 
## Instance

### Entities

1. **Bank**
   - **Country**: India
   - **Name**: State Bank of India
   - **BIC**: SBININBBXXX

2. **Accounts**
   - **Account 1**
     - **IBAN**: IN4321098765432109876543210
     - **Balance**: 3000
   - **Account 2**
     - **IBAN**: IN5432109876543210987654321
     - **Balance**: 15000
   - **Account 3**
     - **IBAN**: IN6543210987654321098765432
     - **Balance**: 45000
   - **Account 4**
     - **IBAN**: IN7654321098765432109876543
     - **Balance**: 600
   - **Account 5**
     - **IBAN**: IN8765432109876543210987654
     - **Balance**: 12000
   - **Account 6**
     - **IBAN**: IN9876543210987654321098765
     - **Balance**: 85000

3. **Persons**
   - **Person 1**
     - **First Name**: Rohan
     - **Last Name**: Sharma
     - **Age**: 29
   - **Person 2**
     - **First Name**: Priya
     - **Last Name**: Kapoor
     - **Age**: 35
   - **Person 3**
     - **First Name**: Arjun
     - **Last Name**: Iyer
     - **Age**: 42
   - **Person 4**
     - **First Name**: Anjali
     - **Last Name**: Gupta
     - **Age**: 37
   - **Person 5**
     - **First Name**: Vivek
     - **Last Name**: Rao
     - **Age**: 31

### Relationships

1. **Ownership Association**
   - **Account 1** is owned by:
     - **Rohan Sharma**
   - **Account 2** is owned by:
     - **Priya Kapoor**
     - **Arjun Iyer**
   - **Account 3** is owned by:
     - **Anjali Gupta**
     - **Rohan Sharma**
   - **Account 4** is owned by:
     - **Vivek Rao**
   - **Account 5** is owned by:
     - **Priya Kapoor**
   - **Account 6** is owned by:
     - **Anjali Gupta**
     - **Vivek Rao**

2. **Use Association**
   - **Account 1** is used by:
     - **Rohan Sharma**
     - **Priya Kapoor**
   - **Account 2** is used by:
     - **Arjun Iyer**
     - **Anjali Gupta**
   - **Account 3** is used by:
     - **Rohan Sharma**
     - **Vivek Rao**
   - **Account 4** is used by:
     - **Vivek Rao**
   - **Account 5** is used by:
     - **Priya Kapoor**
     - **Anjali Gupta**
   - **Account 6** is used by:
     - **Anjali Gupta**
     - **Rohan Sharma**

3. **AccountOfBanks Composition**
   - **Account 1**, **Account 2**, **Account 3**, **Account 4**, **Account 5**, and **Account 6** are all associated with:
     - **State Bank of India**

### Notes

- This instance presents an Indian context, featuring typical Indian names and the country's largest bank, adding a different geographic and cultural dimension.
- Structurally, it is complex with six accounts and five individuals, offering a wide range of ownership and user scenarios, including both single and dual ownerships.
- The model adheres to all constraints: every account has non-negative balances, and all owners are of legal age.
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('stateBankOfIndia')
!stateBankOfIndia.country := 'India'
!stateBankOfIndia.name := 'State Bank of India'
!stateBankOfIndia.bic := 'SBININBBXXX'

!new Account('account18_1')
!account18_1.iban := 'IN4321098765432109876543210'
!account18_1.balance := 3000

!new Account('account18_2')
!account18_2.iban := 'IN5432109876543210987654321'
!account18_2.balance := 15000

!new Account('account18_3')
!account18_3.iban := 'IN6543210987654321098765432'
!account18_3.balance := 45000

!new Account('account18_4')
!account18_4.iban := 'IN7654321098765432109876543'
!account18_4.balance := 600

!new Account('account18_5')
!account18_5.iban := 'IN8765432109876543210987654'
!account18_5.balance := 12000

!new Account('account18_6')
!account18_6.iban := 'IN9876543210987654321098765'
!account18_6.balance := 85000

!new Person('rohanSharma')
!rohanSharma.firstName := 'Rohan'
!rohanSharma.lastName := 'Sharma'
!rohanSharma.age := 29

!new Person('priyaKapoor')
!priyaKapoor.firstName := 'Priya'
!priyaKapoor.lastName := 'Kapoor'
!priyaKapoor.age := 35

!new Person('arjunIyer')
!arjunIyer.firstName := 'Arjun'
!arjunIyer.lastName := 'Iyer'
!arjunIyer.age := 42

!new Person('anjaliGupta')
!anjaliGupta.firstName := 'Anjali'
!anjaliGupta.lastName := 'Gupta'
!anjaliGupta.age := 37

!new Person('vivekRao')
!vivekRao.firstName := 'Vivek'
!vivekRao.lastName := 'Rao'
!vivekRao.age := 31

!insert (stateBankOfIndia, account18_1) into AccountOfBanks
!insert (stateBankOfIndia, account18_2) into AccountOfBanks
!insert (stateBankOfIndia, account18_3) into AccountOfBanks
!insert (stateBankOfIndia, account18_4) into AccountOfBanks
!insert (stateBankOfIndia, account18_5) into AccountOfBanks
!insert (stateBankOfIndia, account18_6) into AccountOfBanks

!insert (rohanSharma, account18_1) into Ownership

!insert (priyaKapoor, account18_2) into Ownership
!insert (arjunIyer, account18_2) into Ownership

!insert (anjaliGupta, account18_3) into Ownership
!insert (rohanSharma, account18_3) into Ownership

!insert (vivekRao, account18_4) into Ownership

!insert (priyaKapoor, account18_5) into Ownership

!insert (anjaliGupta, account18_6) into Ownership
!insert (vivekRao, account18_6) into Ownership

!insert (rohanSharma, account18_1) into Use
!insert (priyaKapoor, account18_1) into Use

!insert (arjunIyer, account18_2) into Use
!insert (anjaliGupta, account18_2) into Use

!insert (rohanSharma, account18_3) into Use
!insert (vivekRao, account18_3) into Use

!insert (vivekRao, account18_4) into Use

!insert (priyaKapoor, account18_5) into Use
!insert (anjaliGupta, account18_5) into Use

!insert (anjaliGupta, account18_6) into Use
!insert (rohanSharma, account18_6) into Use
</object_model>
No judge system prompt available
No judge user prompt available
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The object model represents a highly plausible banking scenario. The bank, persons, and account details use valid and coherent data (e.g., ages are all over 18, balances are positive, names and BIC codes match the Indian bank context). The relationships properly reflect constraints, such as accounts having exactly one or two owners, and realistic usage delegation (people using accounts they do not exclusively own).

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.08
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/68
Multiplicities 0/26
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 89.0%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 12/∞
Attributes 30/30
Relationships 26/∞
Viewer
!new Bank('nileDeltaBank')
!nileDeltaBank.country := 'Egypt'
!nileDeltaBank.name := 'NileDelta Bank'
!nileDeltaBank.bic := 'NILDELBK001'

!new Account('account21')
!account21.iban := 'EG64NILDB000000123456789045'
!account21.balance := 100

!new Person('aminaHassan')
!aminaHassan.firstName := 'Amina'
!aminaHassan.lastName := 'Hassan'
!aminaHassan.age := 22

!new Person('youssefAli')
!youssefAli.firstName := 'Youssef'
!youssefAli.lastName := 'Ali'
!youssefAli.age := 23

!new Person('saraMahmoud')
!saraMahmoud.firstName := 'Sara'
!saraMahmoud.lastName := 'Mahmoud'
!saraMahmoud.age := 21

!insert (nileDeltaBank, account21) into AccountOfBanks

!insert (aminaHassan, account21) into Ownership

!insert (aminaHassan, account21) into Use
!insert (youssefAli, account21) into Use
!insert (saraMahmoud, account21) into Use
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand.  You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose.  ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association.  ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled).
Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes.
You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created.  Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
Analyze the following UML class diagram:
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
For the same category and model description, generate another instance that is structurally and semantically different from the previous ones
Continue with the following description, creating the instance according to the syntax example and this specification: 
**Instance Description:**

In the historical and vibrant capital of Egypt, a reputable bank known as **"NileDelta Bank"** thrives by blending modern banking practices with regional traditions. Here are the details of NileDelta Bank:

- **Bank**:
  - `country`: "Egypt"
  - `name`: "NileDelta Bank"
  - `bic`: "NILDELBK001"

Within NileDelta Bank, there lies an account with a minimalistic approach to banking designed for everyday utilities and small transactions:

- **Account**:
  - `iban`: "EG64NILDB000000123456789045"
  - `balance`: 100 (An account primarily used for day-to-day expenditures and minor savings)

This account is owned by a single individual, focusing on singular management and individual banking needs, examining the boundaries of simplicity and essential banking:

1. **Person (Sole Owner)**:
   - `firstName`: "Amina"
   - `lastName`: "Hassan"
   - `age`: 22

Amina precisely embodies the requirement of adulthood, steering the account with independence, central to daily life expenses and learning financial management early in life.

Additionally, the account is actively used by a diverse group of non-owner users, reflecting extended family interactions and support typical in closely-knit communities:

2. **Person (User, not an owner)**:
   - `firstName`: "Youssef"
   - `lastName`: "Ali"
   - `age`: 23

3. **Person (User, not an owner)**:
   - `firstName`: "Sara"
   - `lastName`: "Mahmoud"
   - `age`: 21

Both Youssef and Sara, while not owning the account, contribute and engage through limited roles, showcasing flexibility and support among peers for everyday financial needs.

This scenario illustrates:
- A modest, personal account balance focused on essential spending.
- Sole ownership offering personal financial autonomy.
- Inclusion of peer support and usage, reflecting communal ties in financial practices integrating both personal independence and community assistance.
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('nileDeltaBank')
!nileDeltaBank.country := 'Egypt'
!nileDeltaBank.name := 'NileDelta Bank'
!nileDeltaBank.bic := 'NILDELBK001'

!new Account('account21')
!account21.iban := 'EG64NILDB000000123456789045'
!account21.balance := 100

!new Person('aminaHassan')
!aminaHassan.firstName := 'Amina'
!aminaHassan.lastName := 'Hassan'
!aminaHassan.age := 22

!new Person('youssefAli')
!youssefAli.firstName := 'Youssef'
!youssefAli.lastName := 'Ali'
!youssefAli.age := 23

!new Person('saraMahmoud')
!saraMahmoud.firstName := 'Sara'
!saraMahmoud.lastName := 'Mahmoud'
!saraMahmoud.age := 21

!insert (nileDeltaBank, account21) into AccountOfBanks

!insert (aminaHassan, account21) into Ownership

!insert (aminaHassan, account21) into Use
!insert (youssefAli, account21) into Use
!insert (saraMahmoud, account21) into Use
</object_model>
No judge system prompt available
No judge user prompt available
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The object model represents a highly plausible scenario. The bank, names, and IBAN format are consistent with the Egyptian context. The account balance is positive, the owner is an adult (satisfying the model's constraints), and it makes logical sense for an account to have one primary owner but multiple users who are granted access.

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.08
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/24
Multiplicities 0/5
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 93.1%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 5/∞
Attributes 14/14
Relationships 5/∞
Viewer
!new Bank('warszawskiBank')
!warszawskiBank.country := 'Poland'
!warszawskiBank.name := 'Warszawski Bank'
!warszawskiBank.bic := 'WBPL10456'

!new Bank('emiratesFinanceCorp')
!emiratesFinanceCorp.country := 'United Arab Emirates'
!emiratesFinanceCorp.name := 'Emirates Finance Corporation'
!emiratesFinanceCorp.bic := 'EFUAE67890'

!new Person('katarzynaNowak')
!katarzynaNowak.firstName := 'Katarzyna'
!katarzynaNowak.lastName := 'Nowak'
!katarzynaNowak.age := 38

!new Person('abdulAlFarsi')
!abdulAlFarsi.firstName := 'Abdul'
!abdulAlFarsi.lastName := 'Al-Farsi'
!abdulAlFarsi.age := 45

!new Person('lechMazur')
!lechMazur.firstName := 'Lech'
!lechMazur.lastName := 'Mazur'
!lechMazur.age := 53

!new Person('fatimaKhan')
!fatimaKhan.firstName := 'Fatima'
!fatimaKhan.lastName := 'Khan'
!fatimaKhan.age := 31

!new Person('zofiaKwiatkowska')
!zofiaKwiatkowska.firstName := 'Zofia'
!zofiaKwiatkowska.lastName := 'Kwiatkowska'
!zofiaKwiatkowska.age := 50

!new Account('account28_1')
!account28_1.iban := 'PL9088776655A'
!account28_1.balance := 4500

!new Account('account28_2')
!account28_2.iban := 'AE4455667788B'
!account28_2.balance := 9100

!new Account('account28_3')
!account28_3.iban := 'PL3322110044C'
!account28_3.balance := 12000

!new Account('account28_4')
!account28_4.iban := 'AE9988776644D'
!account28_4.balance := 2500

!new Account('account28_5')
!account28_5.iban := 'PL6677889900E'
!account28_5.balance := 0

!insert (warszawskiBank, account28_1) into AccountOfBanks
!insert (emiratesFinanceCorp, account28_2) into AccountOfBanks
!insert (warszawskiBank, account28_3) into AccountOfBanks
!insert (emiratesFinanceCorp, account28_4) into AccountOfBanks
!insert (warszawskiBank, account28_5) into AccountOfBanks

!insert (katarzynaNowak, account28_1) into Ownership

!insert (abdulAlFarsi, account28_2) into Ownership

!insert (lechMazur, account28_3) into Ownership
!insert (zofiaKwiatkowska, account28_3) into Ownership

!insert (fatimaKhan, account28_4) into Ownership
!insert (abdulAlFarsi, account28_4) into Ownership

!insert (katarzynaNowak, account28_5) into Ownership
!insert (lechMazur, account28_5) into Ownership

!insert (katarzynaNowak, account28_1) into Use
!insert (lechMazur, account28_1) into Use

!insert (abdulAlFarsi, account28_2) into Use
!insert (fatimaKhan, account28_2) into Use

!insert (lechMazur, account28_3) into Use
!insert (zofiaKwiatkowska, account28_3) into Use
!insert (katarzynaNowak, account28_3) into Use

!insert (fatimaKhan, account28_4) into Use

!insert (katarzynaNowak, account28_5) into Use
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand.  You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose.  ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association.  ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled).
Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes.
You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created.  Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
Analyze the following UML class diagram:
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
For the same category and model description, generate another instance that is structurally and semantically different from the previous ones
Continue with the following description, creating the instance according to the syntax example and this specification: 
### Complex Instance: An Eastern European and Middle Eastern Banking Scenario

#### Banks
1. **Bank1:**
   - **Country:** Poland
   - **Name:** Warszawski Bank
   - **BIC:** WBPL10456

2. **Bank2:**
   - **Country:** United Arab Emirates
   - **Name:** Emirates Finance Corporation
   - **BIC:** EFUAE67890

#### Persons
1. **Person1:**
   - **First Name:** Katarzyna
   - **Last Name:** Nowak
   - **Age:** 38

2. **Person2:**
   - **First Name:** Abdul
   - **Last Name:** Al-Farsi
   - **Age:** 45

3. **Person3:**
   - **First Name:** Lech
   - **Last Name:** Mazur
   - **Age:** 53

4. **Person4:**
   - **First Name:** Fatima
   - **Last Name:** Khan
   - **Age:** 31

5. **Person5:**
   - **First Name:** Zofia
   - **Last Name:** Kwiatkowska
   - **Age:** 50

#### Accounts
1. **Account1:**
   - **IBAN:** PL9088776655A
   - **Balance:** 4500
   - **Bank:** Warszawski Bank
   - **Owners:** Katarzyna Nowak
   - **Users:** Katarzyna Nowak, Lech Mazur

2. **Account2:**
   - **IBAN:** AE4455667788B
   - **Balance:** 9100
   - **Bank:** Emirates Finance Corporation
   - **Owners:** Abdul Al-Farsi
   - **Users:** Abdul Al-Farsi, Fatima Khan

3. **Account3:**
   - **IBAN:** PL3322110044C
   - **Balance:** 12000
   - **Bank:** Warszawski Bank
   - **Owners:** Lech Mazur, Zofia Kwiatkowska
   - **Users:** Lech Mazur, Zofia Kwiatkowska, Katarzyna Nowak

4. **Account4:**
   - **IBAN:** AE9988776644D
   - **Balance:** 2500
   - **Bank:** Emirates Finance Corporation
   - **Owners:** Fatima Khan, Abdul Al-Farsi
   - **Users:** Fatima Khan

5. **Account5:**
   - **IBAN:** PL6677889900E
   - **Balance:** 0
   - **Bank:** Warszawski Bank
   - **Owners:** Katarzyna Nowak, Lech Mazur
   - **Users:** Katarzyna Nowak

#### Description
- **Warszawski Bank** in Poland and **Emirates Finance Corporation** in the UAE provide significant banking services to their respective regions, with a focus on both personal and commercial financial needs.
- **Katarzyna Nowak** keeps an account with a considerable balance at Warszawski Bank, which also benefits **Lech Mazur** through shared financial activities, suggesting cooperative investment or familial support.
- **Abdul Al-Farsi** commands a well-funded account with Emirates Finance Corporation, shared with **Fatima Khan**, indicating regional investment interests or business collaborations typical in the UAE's financial environment.
- **Lech Mazur** and **Zofia Kwiatkowska** co-manage a high-value account at Warszawski Bank, involving **Katarzyna Nowak** as a user, showcasing cross-utilization for personal investments or collective family funds.
- **Fatima Khan** and **Abdul Al-Farsi** share a joint account with Emirates Finance Corporation, a common practice in the Middle East for managing family finances or co-owned business revenue.
- **Katarzyna Nowak** and **Lech Mazur** collectively own a dormant account at Warszawski Bank, indicating potential future financial plans or reserved funds awaiting activation.

This scenario typifies financial behavior in Eastern Europe and the Middle East, emphasizing individual and communal financial strategies, with joint accounts supporting both personal and entrepreneurial aspirations across culturally diverse communities.
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('warszawskiBank')
!warszawskiBank.country := 'Poland'
!warszawskiBank.name := 'Warszawski Bank'
!warszawskiBank.bic := 'WBPL10456'

!new Bank('emiratesFinanceCorp')
!emiratesFinanceCorp.country := 'United Arab Emirates'
!emiratesFinanceCorp.name := 'Emirates Finance Corporation'
!emiratesFinanceCorp.bic := 'EFUAE67890'

!new Person('katarzynaNowak')
!katarzynaNowak.firstName := 'Katarzyna'
!katarzynaNowak.lastName := 'Nowak'
!katarzynaNowak.age := 38

!new Person('abdulAlFarsi')
!abdulAlFarsi.firstName := 'Abdul'
!abdulAlFarsi.lastName := 'Al-Farsi'
!abdulAlFarsi.age := 45

!new Person('lechMazur')
!lechMazur.firstName := 'Lech'
!lechMazur.lastName := 'Mazur'
!lechMazur.age := 53

!new Person('fatimaKhan')
!fatimaKhan.firstName := 'Fatima'
!fatimaKhan.lastName := 'Khan'
!fatimaKhan.age := 31

!new Person('zofiaKwiatkowska')
!zofiaKwiatkowska.firstName := 'Zofia'
!zofiaKwiatkowska.lastName := 'Kwiatkowska'
!zofiaKwiatkowska.age := 50

!new Account('account28_1')
!account28_1.iban := 'PL9088776655A'
!account28_1.balance := 4500

!new Account('account28_2')
!account28_2.iban := 'AE4455667788B'
!account28_2.balance := 9100

!new Account('account28_3')
!account28_3.iban := 'PL3322110044C'
!account28_3.balance := 12000

!new Account('account28_4')
!account28_4.iban := 'AE9988776644D'
!account28_4.balance := 2500

!new Account('account28_5')
!account28_5.iban := 'PL6677889900E'
!account28_5.balance := 0

!insert (warszawskiBank, account28_1) into AccountOfBanks
!insert (emiratesFinanceCorp, account28_2) into AccountOfBanks
!insert (warszawskiBank, account28_3) into AccountOfBanks
!insert (emiratesFinanceCorp, account28_4) into AccountOfBanks
!insert (warszawskiBank, account28_5) into AccountOfBanks

!insert (katarzynaNowak, account28_1) into Ownership

!insert (abdulAlFarsi, account28_2) into Ownership

!insert (lechMazur, account28_3) into Ownership
!insert (zofiaKwiatkowska, account28_3) into Ownership

!insert (fatimaKhan, account28_4) into Ownership
!insert (abdulAlFarsi, account28_4) into Ownership

!insert (katarzynaNowak, account28_5) into Ownership
!insert (lechMazur, account28_5) into Ownership

!insert (katarzynaNowak, account28_1) into Use
!insert (lechMazur, account28_1) into Use

!insert (abdulAlFarsi, account28_2) into Use
!insert (fatimaKhan, account28_2) into Use

!insert (lechMazur, account28_3) into Use
!insert (zofiaKwiatkowska, account28_3) into Use
!insert (katarzynaNowak, account28_3) into Use

!insert (fatimaKhan, account28_4) into Use

!insert (katarzynaNowak, account28_5) into Use
</object_model>
No judge system prompt available
No judge user prompt available
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The object model uses plausible values for all attributes, including names and ages of adults. The IBANs correctly match the countries of their respective banks (e.g., 'PL' prefix for the Polish bank and 'AE' for the UAE bank), positive balances are kept, and the constraints regarding the maximum number of account owners are respected. Authorized users ('Use' association) acting on accounts they don't explicitly own also reflects a standard real-world banking scenario.

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.12
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/65
Multiplicities 0/22
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 93.1%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 12/∞
Attributes 31/31
Relationships 22/∞
Viewer
!new Bank('balticDigitalBank')
!balticDigitalBank.country := 'Estonia'
!balticDigitalBank.name := 'Baltic Digital Bank'
!balticDigitalBank.bic := 'BALTETEE01'

!new Account('account27_1')
!account27_1.iban := 'EE98-7654-3212-0004'
!account27_1.balance := 200000

!new Account('account27_2')
!account27_2.iban := 'EE12-3456-7890-5678'
!account27_2.balance := 3500

!new Account('account27_3')
!account27_3.iban := 'EE34-5678-9012-7890'
!account27_3.balance := 15000

!new Person('erikSavisaar')
!erikSavisaar.firstName := 'Erik'
!erikSavisaar.lastName := 'Savisaar'
!erikSavisaar.age := 38

!new Person('katarinaPetrov')
!katarinaPetrov.firstName := 'Katarina'
!katarinaPetrov.lastName := 'Petrov'
!katarinaPetrov.age := 29

!new Person('miguelAlvarez')
!miguelAlvarez.firstName := 'Miguel'
!miguelAlvarez.lastName := 'Alvarez'
!miguelAlvarez.age := 42

!insert (balticDigitalBank, account27_1) into AccountOfBanks
!insert (balticDigitalBank, account27_2) into AccountOfBanks
!insert (balticDigitalBank, account27_3) into AccountOfBanks

!insert (erikSavisaar, account27_1) into Ownership
!insert (katarinaPetrov, account27_1) into Ownership

!insert (katarinaPetrov, account27_2) into Ownership

!insert (miguelAlvarez, account27_3) into Ownership

!insert (erikSavisaar, account27_1) into Use
!insert (katarinaPetrov, account27_1) into Use
!insert (miguelAlvarez, account27_1) into Use

!insert (erikSavisaar, account27_2) into Use

!insert (katarinaPetrov, account27_3) into Use
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand.  You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose.  ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association.  ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled).
Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes.
You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created.  Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
Analyze the following UML class diagram:
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
For the same category and model description, generate another instance that is structurally and semantically different from the previous ones
Continue with the following description, creating the instance according to the syntax example and this specification: 
**Instance: Digital Nomad Group Portfolio Management**

### Entities

- **Bank:**
  - `country`: Estonia
  - `name`: Baltic Digital Bank
  - `bic`: BALTETEE01

- **Person:**
  - `firstName`: Erik
  - `lastName`: Savisaar
  - `age`: 38

- **Person:**
  - `firstName`: Katarina
  - `lastName`: Petrov
  - `age`: 29

- **Person:**
  - `firstName`: Miguel
  - `lastName`: Alvarez
  - `age`: 42

- **Account:**
  - `iban`: EE98-7654-3212-0004
  - `balance`: 200_000

- **Account:**
  - `iban`: EE12-3456-7890-5678
  - `balance`: 3_500

- **Account:**
  - `iban`: EE34-5678-9012-7890
  - `balance`: 15_000

### Relationships

- **Ownership Association:**
  - **Account EE98-7654-3212-0004**:
    - Owners: Erik Savisaar, Katarina Petrov, Miguel Alvarez (A collective investment account managed by three entrepreneurial digital nomads pooling their resources to manage a diverse investment portfolio across industries.)

- **Ownership Association:**
  - **Account EE12-3456-7890-5678**:
    - Owners: Katarina Petrov (An individual account for Katarina to manage daily expenses related to her freelance work and travel.)

- **Ownership Association:**
  - **Account EE34-5678-9012-7890**:
    - Owners: Miguel Alvarez (A separate account to handle income from consultancy work across Europe, maintaining financial solvency while on the move.)

- **Use Association:**
  - **Account EE98-7654-3212-0004**:
    - Users: Erik Savisaar, Katarina Petrov, Miguel Alvarez (All owners actively engage in decision-making for strategic investment choices and funds allocation, reflecting their collaborative dynamic.)

- **Use Association:**
  - **Account EE12-3456-7890-5678**:
    - Users: Erik Savisaar (Erik assists Katarina with financial advisory to maximize her expense management while ensuring her ventures remain profitable.)

- **Use Association:**
  - **Account EE34-5678-9012-7890**:
    - Users: Katarina Petrov (Katarina supports Miguel in bookkeeping and documenting financial interactions to ensure compliant reporting and fiscal health.)

- **AccountOfBanks Composition:**
  - **Baltic Digital Bank:**
    - Accounts: EE98-7654-3212-0004, EE12-3456-7890-5678, EE34-5678-9012-7890

### Explanation

This edge case features a unique setup tailored to the lifestyle of digital nomads focusing on flexibility, independence, and global connectivity:

- The core account EE98-7654-3212-0004, collaboratively owned by all three professionals, emphasizes a modern investment model where like-minded individuals unite their financial strength to leverage investment opportunities, harnessing geo-independent earnings for collective growth.

- Individual accounts EE12-3456-7890-5678 and EE34-5678-9012-7890 reflect the personal financial strategies of Katarina and Miguel, possessing distinct sources of income, from freelance projects to consultancy engagements, which sustain their nomadic needs.

- The dynamic of shared engagement in their joint financial ventures highlights the self-governing, democratic approach to financial management among the group, with roles that balance individual and collaborative interests.

- Use associations cross-pollinate knowledge between experts, enabling cross-support on fiscal strategies and practices outside each person’s core expertise, benefiting from collective intellect while nurturing personal advancement. 

This instance encapsulates a non-traditional financial structure, perfectly suited to the versatility and agility required by digital nomads, emphasizing cooperative growth, individual pursuits, and efficient resource management in a globally connected profession.
The last output is partially incorrect: 
checking structure...
Multiplicity constraint violation in association `Ownership':
  Object `account27_1' of class `Account' is connected to 3 objects of class `Person'
  at association end `owner' but the multiplicity is specified as `1..2'.

Please provide the complete output corrected
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('balticDigitalBank')
!balticDigitalBank.country := 'Estonia'
!balticDigitalBank.name := 'Baltic Digital Bank'
!balticDigitalBank.bic := 'BALTETEE01'

!new Account('account27_1')
!account27_1.iban := 'EE98-7654-3212-0004'
!account27_1.balance := 200000

!new Account('account27_2')
!account27_2.iban := 'EE12-3456-7890-5678'
!account27_2.balance := 3500

!new Account('account27_3')
!account27_3.iban := 'EE34-5678-9012-7890'
!account27_3.balance := 15000

!new Person('erikSavisaar')
!erikSavisaar.firstName := 'Erik'
!erikSavisaar.lastName := 'Savisaar'
!erikSavisaar.age := 38

!new Person('katarinaPetrov')
!katarinaPetrov.firstName := 'Katarina'
!katarinaPetrov.lastName := 'Petrov'
!katarinaPetrov.age := 29

!new Person('miguelAlvarez')
!miguelAlvarez.firstName := 'Miguel'
!miguelAlvarez.lastName := 'Alvarez'
!miguelAlvarez.age := 42

!insert (balticDigitalBank, account27_1) into AccountOfBanks
!insert (balticDigitalBank, account27_2) into AccountOfBanks
!insert (balticDigitalBank, account27_3) into AccountOfBanks

!insert (erikSavisaar, account27_1) into Ownership
!insert (katarinaPetrov, account27_1) into Ownership

!insert (katarinaPetrov, account27_2) into Ownership

!insert (miguelAlvarez, account27_3) into Ownership

!insert (erikSavisaar, account27_1) into Use
!insert (katarinaPetrov, account27_1) into Use
!insert (miguelAlvarez, account27_1) into Use

!insert (erikSavisaar, account27_2) into Use

!insert (katarinaPetrov, account27_3) into Use
</object_model>
No judge system prompt available
No judge user prompt available
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The scenario models a valid Estonian bank with properly formatted 'EE' IBANs and correct BIC structures. The account balances are realistic positive integers, all owners are adults, and the distinction between account owners and non-owning authorized users reflects sensible real-world banking practices.

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.21
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/37
Multiplicities 0/12
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 91.3%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 7/∞
Attributes 18/18
Relationships 12/∞
Viewer
!new Bank('unityCooperativeBank')
!unityCooperativeBank.country := 'Kenya'
!unityCooperativeBank.name := 'Unity Cooperative Bank'
!unityCooperativeBank.bic := 'UCBKE44'

!new Account('account23_9')
!account23_9.iban := 'KE74 5678 1234 0001'
!account23_9.balance := 5000

!new Account('account23_10')
!account23_10.iban := 'KE55 2345 6789 0012'
!account23_10.balance := 7500

!new Person('aminaKareem')
!aminaKareem.firstName := 'Amina'
!aminaKareem.lastName := 'Kareem'
!aminaKareem.age := 52

!new Person('victorMwangi')
!victorMwangi.firstName := 'Victor'
!victorMwangi.lastName := 'Mwangi'
!victorMwangi.age := 47

!new Person('graceAchola')
!graceAchola.firstName := 'Grace'
!graceAchola.lastName := 'Achola'
!graceAchola.age := 30

!new Person('elijahWanjiku')
!elijahWanjiku.firstName := 'Elijah'
!elijahWanjiku.lastName := 'Wanjiku'
!elijahWanjiku.age := 32

!insert (unityCooperativeBank, account23_9) into AccountOfBanks
!insert (unityCooperativeBank, account23_10) into AccountOfBanks

!insert (aminaKareem, account23_9) into Ownership
!insert (victorMwangi, account23_9) into Ownership

!insert (aminaKareem, account23_10) into Ownership

!insert (aminaKareem, account23_9) into Use
!insert (victorMwangi, account23_9) into Use
!insert (graceAchola, account23_9) into Use
!insert (elijahWanjiku, account23_9) into Use

!insert (aminaKareem, account23_10) into Use
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
You are tasked with analyzing conceptual models represented as class diagrams and expressed in the UML-based specification environment using its native syntax. You must adhere to the following requirements: - Use very clear language. - Do not overexplain, be concise. - Multiplicities must be very clear and easy to understand.  You should follow the structure and requirements below: ## Description Start by explaining the overall structure and purpose of the model. ### Components Break down the components of the model (i.e., classes and attributes), describing each, their type and purpose.  ## Relationships Describe the relationships between the components of the model, dependencies and multiplicities (i.e., minimum and maximum number of instances of one class that can be associated with instances of another class). Describe the multiplicities at both ends of each association.  ## Invariants Define the invariants that apply to the model (i.e., those constraints that must be fulfilled).
Your task is to generate a complete and diverse instance, in plain English, for a given category and based on a provided conceptual model description. The instance must adhere to these requirements: - Be self-contained: Include all required attributes, relationships, and related entities in full detail. - Conform to the model: Fulfill the constraints, multiplicities, relationships, and attributes defined in the class diagram model. - Understand the context: Ensure that its attributes and relationships are relevant. - Avoid duplication of instances: Take into consideration those instances previously built to avoid redundancy. - Semantic diversity: From a semantic point of view, incorporate varied scenarios, including regional, linguistic, or cultural differences. - Structural diversity: Include instances with different numbers of elements, different numbers of relationships and complexity, and create varied examples by changing entity attributes.
You are tasked with creating instances of a conceptual model in the UML-based Specification Environment (USE). You will receive: 1. The UML class diagram that the instance follows. 2. A sample syntax of instances creation. 3. A description of the instance that needs to be created.  Your goal is to generate these instances based on the provided description, adhering strictly to these requirements: - The output must be in plain text, with no additional comments, descriptions, or explanations. - Ensure that the created instance adheres to the provided description. - Follow the syntax sample provided, without deviation. - Take into account previously created instances to avoid using duplicate naming.
<role>
You are an expert software and system modeler. You are able to assess the semantic quality of object models that have been created to conform to a domain model. The models are defined in USE (UML-based Specification Environment) and OCL (Object Constraint Language).

Your primary capability is "Semantic Reality Checking". You do not just check for syntactic correctness; you check for real-world plausibility and logical consistency within a given domain.
</role>

<context>
The user will provide two types of content:
1. **Domain Model (.use)**: A class diagram definition including classes, attributes, enums, relationships, multiplicities and roles.
2. **Object Model (.soil)**: An object model. This object model can be seen as a script composed of instructions for the creation of objects, relationships and setting attribute values (snapshot).

Your goal is to act as a judge to determine if the object model represents a **REALISTIC** scenario based on the domain model and common sense real-world logic.
</context>

<definitions>
- **Realistic**: The object model is syntactically correct AND semantically plausible (e.g., A 'Person' has an age between 0 and 120; a 'Car' has a positive price).
- **Unrealistic**: The object model contains contradictions, impossible physical values, or nonsensical relationships (e.g., A 'Person' is their own father; a 'Product' has a negative weight).
- **Doubtful**: You cannot determine whether the object model is realistic or not.
</definitions>

<instructions>
Follow this thinking process strictly before generating the final output:

1. **Analyze the Domain (.use)**: Understand the classes and what they represent in the real world.
2. **Analyze the Instances (.soil)**: Map the created objects to their classes. Look at the specific values assigned to attributes and the relationships created between objects.
3. **Evaluate Semantics**:
    - Apply "Common Sense Knowledge" to the attribute values.
    - Check cardinality and relationship logic beyond simple OCL constraints.
    - Identify any outliers or logical fallacies.
4. **Determine Verdict**: Select one of the defined labels (Realistic/Unrealistic/Doubtful).
</instructions>

<constraints>
- **Tone**: Objective, Analytical, Technical.
- **Verbosity**: Low. Be direct.
- **Reasoning**: The "Why" section must be concise and specific, citing variable names, objects, or relationships when possible.
- Do not output the internal thinking process. Only output the final formatted result.
</constraints>

<output_format>
Structure your response exactly as follows:

**Response**: [Realistic | Unrealistic | Doubtful]
**Why**: [Concise explanation of your reasoning. If Unrealistic, specify the exact objects, values or relationships that break realism.]
</output_format>

<examples>
Example 1:
Input:

<domain_model>
class Person
attributes
    age: Integer
end
class Pet
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Pet [*] role pets
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 250
!new Pet('pet1')
!pet1.name := 'Luna'
… 1.000 more pets creation …
!pet1000.name := 'Max'
!insert (p1, pet1) into Ownership
…1.000 more pets associated with p1 …
!insert (p1, pet1000) into Ownership
</object_model>

Output:

**Response**: Unrealistic
**Why**: The object 'p1' of class 'Person' has an age of 250, which exceeds the biologically plausible lifespan of a human. Although it is not plausible that 1 same person owns 1.000 pets.


Example 2:
Input:

<domain_model>
class Car
attributes
    brand: String
end
class Person
attributes
    name: String
end
association Ownership between
    Person [1] role owner
    Car [*] role cars
end
</domain_model>
<object_model>
!new Person('p1')
!p1.age := 19
!new Car('c1')
!c1.brand := 'Toyota'
!insert (p1, c1) into Ownership
</object_model>

Output:

**Response**: Realistic
**Why**: The object 'c1' has a valid, recognized real-world car brand assigned, and its plausible that a teenager has only one car.


Example 3:
Input:

<domain_model>
class Component
attributes
    setting_val: Integer
    config_mode: String
end
</domain_model>
<object_model>
!new Component('c1')
!c1.setting_val := 8080
!c1.config_mode := 'Legacy'
</object_model>

Output:

**Response**: Doubtful
**Why**: The class 'Component' and attribute 'setting_val' are generic and lack specific real-world semantic context. Without knowing what specific physical or software system this represents, it is impossible to determine if '8080' is a realistic value or an outlier.

</examples>
Analyze the following UML class diagram:
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
For the same category and model description, generate another instance that is structurally and semantically different from the previous ones
Continue with the following description, creating the instance according to the syntax example and this specification: 
## Scenario: Non-Profit Organization with Volunteer Access

### Banks
- **Bank Cooperative**
  - `country`: "Kenya"
  - `name`: "Unity Cooperative Bank"
  - `bic`: "UCBKE44"

### Persons
- **Person N**
  - `firstName`: "Amina"
  - `lastName`: "Kareem"
  - `age`: 52

- **Person O**
  - `firstName`: "Victor"
  - `lastName`: "Mwangi"
  - `age`: 47

- **Person P**
  - `firstName`: "Grace"
  - `lastName`: "Achola"
  - `age`: 30

- **Person Q**
  - `firstName`: "Elijah"
  - `lastName`: "Wanjiku"
  - `age`: 32

### Accounts

#### Account 9
- **Details**:
  - `iban`: "KE74 5678 1234 0001"
  - `balance`: 5000
- **Bank Association**:
  - Associated with **Bank Cooperative**
- **Ownership**:
  - Owned by **Person N** and **Person O**
- **Use**:
  - Used by **Person N**, **Person O**, **Person P**, and **Person Q**

#### Account 10
- **Details**:
  - `iban`: "KE55 2345 6789 0012"
  - `balance`: 7500
- **Bank Association**:
  - Associated with **Bank Cooperative**
- **Ownership**:
  - Owned by **Person N**
- **Use**:
  - Used exclusively by **Person N**

### Violations & Overconstraints

1. **High User Count in Account 9**:
   - Account 9 is accessed by four individuals, including volunteers and organizers, reflecting typical non-profit fund management challenges where flexibility is necessary for operational logistics. The model’s strict user boundary could inadequately represent such diverse participative scenarios, indicating overconstraint in actual user multiplicity or responsibilities.

2. **Single Owner Limitation in Account 10**:
   - Owned singly by **Person N**, Account 10 highlights situations where singular ownership oversimplifies ownership dynamics of major accounts, especially in organizations that may need collective decision-making authority, pointing out lack of support for nested authorizations or collective ownership structures beyond two.

3. **Volunteer Access Representation**:
   - Account 9 models access by personnel beyond official owners, relevant in real-world non-profit operations where volunteer engagement is critical. While permissible, limited awareness of access diversity may present information and decision divergences overlooked by current composition rules and owner restrictions.

This instance captures the unique needs of non-profit organizations with various stakeholders requiring access to resources. It reveals challenges in capturing the flexibility required in financial systems supporting altruistic operations through volunteer coordination and evolving management practices, along with intricate access and ownership layers.
<domain_model>
model BankAccount

class Bank
attributes
    country:String
    name:String
    bic:String
end

class Account
attributes
    iban: String
    balance : Integer
end

class Person
attributes
    firstName:String
    lastName:String
    age : Integer
end

association Ownership between
    Person [1..2] role owner
    Account [*] role accounts
end

association Use between
    Person [*] role user
    Account [*]
end

composition AccountOfBanks between
    Bank [1] role bank
    Account [*] role accounts
end

constraints
context Account inv AdultOwners:
    self.owner->forAll(p | p.age >= 18)
	
context Account inv positiveBalance:
    self.balance >= 0
</domain_model>

<object_model>
!new Bank('unityCooperativeBank')
!unityCooperativeBank.country := 'Kenya'
!unityCooperativeBank.name := 'Unity Cooperative Bank'
!unityCooperativeBank.bic := 'UCBKE44'

!new Account('account23_9')
!account23_9.iban := 'KE74 5678 1234 0001'
!account23_9.balance := 5000

!new Account('account23_10')
!account23_10.iban := 'KE55 2345 6789 0012'
!account23_10.balance := 7500

!new Person('aminaKareem')
!aminaKareem.firstName := 'Amina'
!aminaKareem.lastName := 'Kareem'
!aminaKareem.age := 52

!new Person('victorMwangi')
!victorMwangi.firstName := 'Victor'
!victorMwangi.lastName := 'Mwangi'
!victorMwangi.age := 47

!new Person('graceAchola')
!graceAchola.firstName := 'Grace'
!graceAchola.lastName := 'Achola'
!graceAchola.age := 30

!new Person('elijahWanjiku')
!elijahWanjiku.firstName := 'Elijah'
!elijahWanjiku.lastName := 'Wanjiku'
!elijahWanjiku.age := 32

!insert (unityCooperativeBank, account23_9) into AccountOfBanks
!insert (unityCooperativeBank, account23_10) into AccountOfBanks

!insert (aminaKareem, account23_9) into Ownership
!insert (victorMwangi, account23_9) into Ownership

!insert (aminaKareem, account23_10) into Ownership

!insert (aminaKareem, account23_9) into Use
!insert (victorMwangi, account23_9) into Use
!insert (graceAchola, account23_9) into Use
!insert (elijahWanjiku, account23_9) into Use

!insert (aminaKareem, account23_10) into Use
</object_model>
No judge system prompt available
No judge user prompt available
No Shannon Data for this model Available: AddressBook, Football, MyExpenses, PickupNet, Restaurant, VehicleRental, VideoClub
LLM as a Judge
Realistic

The object model provides plausible real-world values for bank details, IBANs, and account balances. The ages of the individuals are normal, and the relationships appropriately reflect standard banking scenarios where accounts can have one or two owners, and multiple authorized users.

Metrics
Stats i
Stats
Breakdown of the total cost and elapsed time for generating the instances.
  • Elapsed Time = Console Time (ie. Processing Time + API Calls)
  • Cost = (input tokens * input price) + (output tokens * output price)
Total Cost $0.09
Validation i
Validation
Measures the correctness of the instantiation using the USE check function.
  • Syntax = 1 - (Total Number of syntax errors [use check] / Total Number of lines [instance])
  • Multiplicities = 1 - (Total Number of multiplicities errors [use check] / Total Number of relationships ([instance] !insert))
  • Invariants = 1 - (Total Number of invariants errors [use check] / Total Number of invariants ([model] constraints))
Syntax 0/36
Multiplicities 0/10
Invariants 0/2
Diversity i
Diversity
Measures the variability of the generated instances. Attributes (NumericEquals, StringEquals, StringLv): It identifies how much the LLM repeats specific values versus generating unique data points across instances (100%: Diverse, 0%: Repetitive). We group all generated attributes into bags (numeric and string) and then perform pairwise comparisons between every element to obtain. Structure (GED): Measures the Graph Edit Distance (GED) similarity between instances. Distribution (Shannon): Measures the entropy and evenness (balanced distribution) of the generated enum values.
  • NumericEquals = Total number of numeric attribute pairs with different values / Total number of possible pairs (n * (n - 1) / 2)
  • StringEquals = Total number of string attribute pairs that are NOT exactly identical / Total number of possible pairs (n * (n - 1) / 2)
  • StringLv = Sum of (Levenshtein Distance(a, b) / max(length(a), length(b))) for all string pairs / Total number of possible pairs (n * (n - 1) / 2)
  • GED = Similarity = 1 - (GED / (0.5 * (GED_to_empty_A + GED_to_empty_B))). 1 = red = identical graphs, <=0.5 = green = different graphs. We consider as edit operations: Nodes, Edges, Node_Labels and Edge_Labels [https://github.com/a-coman/ged]
  • Shannon (Active) = Entropy / log2(Number of unique groups actually generated). Measures how evenly the generated values are distributed, considering only the categories the LLM actually used.
  • Shannon (All) = Entropy / log2(Total number of valid groups defined in the model). Measures how evenly the generated values are distributed against the full spectrum of all possible valid options defined in the .use file.
Numeric 100.0%
String Equals 100.0%
String LV 93.7%
Coverage i
Model Coverage
Measures the breadth of the instantiation. It answers: "How much of the structural blueprint (the model) was used?"
  • Classes = Total Unique Classes instantiated (!new) in the .soil / Total Number of classes (class) in the model .use
  • Attributes = Total Unique Attributes instantiated (!Class.Attribute or !set) in the .soil / Total Number of attributes (attributes) in the model .use
  • Relationships = Total Unique Relationships instantiated (!insert) in the .soil / Total Number of relationships (association, composition, aggregation) in the model .use
Classes 100.0%
Attributes 100.0%
Relationships 100.0%
Instantiation i
Instance Instantiation
Measures the depth or density of the data. It answers: "Of the objects the LLM decided to create, how many of their available 'slots' did it fill?"
  • Classes = Total Number of classes (!new) in the instance / Total possible that could have been instantiated (infinity)
  • Attributes = Total Number of attributes (!Class.Attribute or !set) in the instance / Total possible that could have been instantiated (sum(number of classes instantiated of that type * Class.Attributes))
  • Relationships = Total Number of relationships (!insert) in the instance / Total possible that could have been instantiated (infinity)
Classes 7/∞
Attributes 19/19
Relationships 10/∞