Triple
T816309
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FastAPI |
E17658
|
entity |
| Predicate | uses |
P98
|
FINISHED |
| Object |
Pydantic
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
|
E97057
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Pydantic | Statement: [FastAPI, uses, Pydantic]
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.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Pydantic Triple: [FastAPI, uses, Pydantic]
Generated description
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
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
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a4937bcaac8190a322524ac6f45a5a |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a4ab621d2c819083f10bff4f66c482 |
completed | March 1, 2026, 9:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a76d8d1a448190be8494fa2776615a |
completed | March 3, 2026, 11:23 p.m. |
| NEDg | Description generation | batch_69a78bd0a1d48190907434a17853dfb1 |
completed | March 4, 2026, 1:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a78c3a57d88190a994ed44bcb2d8d1 |
completed | March 4, 2026, 1:34 a.m. |
Created at: March 1, 2026, 7:38 p.m.