Triple
T4276856
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | tiangolo |
E97064
|
entity |
| Predicate | hostsProject |
P2592
|
FINISHED |
| Object | uvicorn-gunicorn-docker |
E97063
|
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: uvicorn-gunicorn-docker | Statement: [tiangolo, hostsProject, uvicorn-gunicorn-docker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: uvicorn-gunicorn-docker Context triple: [tiangolo, hostsProject, uvicorn-gunicorn-docker]
-
A.
Gunicorn (with ASGI workers)
chosen
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.
-
B.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
-
C.
uWSGI
uWSGI is a high-performance application server commonly used to run Python web applications in production, often sitting between web frameworks and web servers like Nginx.
-
D.
FastAPI
FastAPI is a modern, high-performance Python framework for building APIs with automatic interactive documentation and type hint–driven validation.
-
E.
Docker Compose
Docker Compose is a tool that lets you define and run multi-container Docker applications using a simple YAML configuration file.
- 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_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. |
Created at: March 12, 2026, 11:07 p.m.