Triple
T17598364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daphne |
E428631
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | FastAPI |
—
|
NE NERFINISHED |
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: FastAPI | Statement: [Daphne, compatibleWith, FastAPI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FastAPI Context triple: [Daphne, compatibleWith, FastAPI]
-
A.
FastAPI
chosen
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
B.
fastapi-plugins
fastapi-plugins is a collection of reusable extensions and utilities designed to enhance and modularize FastAPI applications.
-
C.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
-
D.
Starlette web framework
Starlette is a lightweight, high-performance ASGI web framework for Python, designed for building asynchronous web services and APIs.
-
E.
full-stack-fastapi-template
full-stack-fastapi-template is a production-ready project scaffold by Sebastián Ramírez (tiangolo) that provides a modern full-stack web application setup using FastAPI, React, and Docker.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1030481909950e140c63255b9 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46c474e5481909d2736241b592dab |
completed | April 19, 2026, 5:46 a.m. |
Created at: April 10, 2026, 5:51 a.m.