Triple
T18015758
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bottle |
E430994
|
entity |
| Predicate | supports |
P516
|
FINISHED |
| Object | uWSGI |
—
|
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: uWSGI | Statement: [Bottle, supports, uWSGI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: uWSGI Context triple: [Bottle, supports, uWSGI]
-
A.
uWSGI
chosen
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.
-
B.
Uvicorn
Uvicorn is a high-performance, ASGI-compatible web server implementation for Python, commonly used to run modern async frameworks and applications.
-
C.
WSGI
WSGI (Web Server Gateway Interface) is a Python standard that defines a common interface between web servers and Python web applications or frameworks.
-
D.
Werkzeug WSGI utility library
Werkzeug WSGI utility library is a comprehensive Python toolkit that provides utilities for building WSGI-compliant web applications and frameworks.
-
E.
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.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b523f588819097389e067dda7f23 |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.