Triple
T4549713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sebastián Ramírez |
E110131
|
entity |
| Predicate | influencedBy |
P9
|
FINISHED |
| Object | Pydantic |
E97057
|
NE FINISHED |
How this triple was built (2 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: [Sebastián Ramírez, influencedBy, Pydantic]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pydantic Context triple: [Sebastián Ramírez, influencedBy, Pydantic]
-
A.
Pydantic
chosen
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
B.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
C.
dataclasses
dataclasses is a Python module that provides a decorator and helper functions for automatically generating boilerplate methods for classes that primarily store data.
-
D.
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.
-
E.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69bd4412524c8190be5bcc9ddee91848 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57f3f8348190868e274ac4df87ce |
completed | March 20, 2026, 2:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdb94cab408190956ef333aa810a3b |
completed | March 20, 2026, 9:17 p.m. |
Created at: March 20, 2026, 1:05 p.m.