dataclasses
E431926
dataclasses is a Python module that provides a decorator and helper functions for automatically generating boilerplate methods for classes that primarily store data.
All labels observed (2)
| Label | Occurrences |
|---|---|
| dataclasses canonical | 1 |
| dataclasses module | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4325201 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: dataclasses Context triple: [Python standard library, includesModule, dataclasses]
-
A.
Pydantic
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
B.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
PEP 484
PEP 484 is the Python Enhancement Proposal that introduced a standard for type hints in Python, forming the basis of the language’s static typing ecosystem.
-
E.
sqlmodel
SQLModel is a Python library by Sebastián Ramírez (tiangolo) that combines SQLAlchemy and Pydantic to provide an easy, type-safe way to define and interact with SQL databases.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: dataclasses Target entity description: dataclasses is a Python module that provides a decorator and helper functions for automatically generating boilerplate methods for classes that primarily store data.
-
A.
Pydantic
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
B.
PEP 572
PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
-
C.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
D.
PEP 484
PEP 484 is the Python Enhancement Proposal that introduced a standard for type hints in Python, forming the basis of the language’s static typing ecosystem.
-
E.
sqlmodel
SQLModel is a Python library by Sebastián Ramírez (tiangolo) that combines SQLAlchemy and Pydantic to provide an easy, type-safe way to define and interact with SQL databases.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf | Python standard library module ⓘ |
| automaticallyGenerates |
__eq__ method
ⓘ
__init__ method ⓘ __repr__ method ⓘ |
| backportAvailableFor | Python 3.6 ⓘ |
| canGenerate |
__hash__ method
ⓘ
ordering methods ⓘ |
| category | data modeling utility ⓘ |
| classConfigurationParameter |
eq
ⓘ
frozen ⓘ init ⓘ order ⓘ slots ⓘ unsafe_hash ⓘ |
| decoratorTarget | user-defined classes ⓘ |
| definedIn | PEP 557 ⓘ |
| documentationLocation | Python official documentation ⓘ |
| fieldConfigurationOption |
compare
ⓘ
default ⓘ default_factory ⓘ hash ⓘ init ⓘ metadata ⓘ repr ⓘ |
| introducedInVersion | Python 3.7 ⓘ |
| moduleName | dataclasses ⓘ |
| partOf | Python standard library ⓘ |
| primaryPurpose | reduce boilerplate in data-holding classes ⓘ |
| programmingLanguage | Python ⓘ |
| providesDecorator | dataclass ⓘ |
| providesFunction |
asdict
ⓘ
astuple ⓘ field ⓘ is_dataclass ⓘ make_dataclass ⓘ replace ⓘ |
| relatedConcept |
attrs library
ⓘ
namedtuple ⓘ |
| runtimeBehavior | processes class annotations at decoration time ⓘ |
| supportsFeature |
default field values
ⓘ
default_factory for fields ⓘ field metadata ⓘ frozen (immutable) classes ⓘ init-only variables (InitVar) ⓘ post-init processing via __post_init__ ⓘ slots-based dataclasses ⓘ type annotations for fields ⓘ |
| typicalUseCase | classes that primarily store data ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: dataclasses Description of subject: dataclasses is a Python module that provides a decorator and helper functions for automatically generating boilerplate methods for classes that primarily store data.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.
linked to: dataclasses