Pydantic
E97057
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
All labels observed (3)
| Label | Occurrences |
|---|---|
| Pydantic canonical | 5 |
| Pydantic v1 | 1 |
| Python typing ecosystem | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T816309 — 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: Pydantic Context triple: [FastAPI, uses, Pydantic]
-
A.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
B.
PyPy
PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
-
C.
PEP 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
Django
Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
- 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: Pydantic Target entity description: Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
A.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
B.
PyPy
PyPy is a high-performance alternative Python interpreter featuring a Just-In-Time (JIT) compiler designed to significantly speed up the execution of Python programs.
-
C.
PEP 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
-
D.
pandas
pandas is a popular open-source Python library that provides powerful, easy-to-use data structures and tools for data analysis and manipulation.
-
E.
Django
Django is a high-level Python web framework that encourages rapid development and clean, pragmatic design for building secure, scalable web applications.
- F. None of above. chosen
Statements (51)
| Predicate | Object |
|---|---|
| instanceOf |
Python library
ⓘ
data validation library ⓘ settings management library ⓘ |
| designGoal |
enable easy data serialization
ⓘ
leverage Python type hints for validation ⓘ provide clear error messages ⓘ |
| license | MIT License ⓘ |
| programmingLanguage | Python ⓘ |
| repositoryPlatform | GitHub ⓘ |
| supports |
BaseModel
ⓘ
JSON Schema generation ⓘ JSON serialization ⓘ ORM mode ⓘ Optional types ⓘ Union types ⓘ alias for fields ⓘ custom data types ⓘ custom encoders ⓘ custom validators ⓘ data deserialization ⓘ data parsing ⓘ data serialization ⓘ data validation ⓘ dataclasses integration ⓘ default values ⓘ environment-based configuration ⓘ error reporting ⓘ field constraints ⓘ field validators ⓘ model configuration ⓘ model copying ⓘ model inheritance ⓘ model re-validation ⓘ model serialization to JSON ⓘ model serialization to dict ⓘ nested models ⓘ root validators ⓘ runtime type checking ⓘ schema generation ⓘ settings classes ⓘ settings management ⓘ strict mode ⓘ type coercion ⓘ typing.Annotated ⓘ validators ⓘ validators via decorators ⓘ |
| typicalUseCase |
API request and response validation
ⓘ
configuration management ⓘ data ingestion pipelines ⓘ modeling domain objects ⓘ |
| uses | Python type hints ⓘ |
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: Pydantic Description of subject: Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
Referenced by (7)
Full triples — surface form annotated when it differs from this entity's canonical label.