Triple
T4276861
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tiangolo |
E97064
|
entity |
| Predicate | hostsProject |
P2592
|
FINISHED |
| Object |
fastapi-plugins
fastapi-plugins is a collection of reusable extensions and utilities designed to enhance and modularize FastAPI applications.
|
E426659
|
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: fastapi-plugins | Statement: [tiangolo, hostsProject, fastapi-plugins]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: fastapi-plugins Context triple: [tiangolo, hostsProject, fastapi-plugins]
-
A.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
B.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
-
C.
Pydantic
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
D.
Gunicorn (with ASGI workers)
Gunicorn (with ASGI workers) is a Python WSGI/ASGI HTTP server that can run asynchronous web frameworks like FastAPI in a robust, production-ready environment.
-
E.
Flask
Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
- 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: fastapi-plugins Triple: [tiangolo, hostsProject, fastapi-plugins]
Generated description
fastapi-plugins is a collection of reusable extensions and utilities designed to enhance and modularize FastAPI applications.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: fastapi-plugins Target entity description: fastapi-plugins is a collection of reusable extensions and utilities designed to enhance and modularize FastAPI applications.
-
A.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
B.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
-
C.
Pydantic
Pydantic is a Python library for data validation and settings management that uses type hints to parse, validate, and serialize data.
-
D.
Gunicorn (with ASGI workers)
Gunicorn (with ASGI workers) is a Python WSGI/ASGI HTTP server that can run asynchronous web frameworks like FastAPI in a robust, production-ready environment.
-
E.
Flask
Flask is a lightweight, flexible Python micro web framework designed for building web applications and APIs with minimal boilerplate.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3501d677481909e7416a1d2b0008c |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5b7b3b52c8190ae7c05448faf5558 |
completed | March 14, 2026, 7:32 p.m. |
| NEDg | Description generation | batch_69b5b95083088190b0c993fa2fbc954c |
completed | March 14, 2026, 7:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b5b9b8afcc8190822cfd560d064590 |
completed | March 14, 2026, 7:40 p.m. |
Created at: March 12, 2026, 11:07 p.m.