Triple

T4276792
Position Surface form Disambiguated ID Type / Status
Subject Gunicorn E97063 entity
Predicate supportsInterface P203 FINISHED
Object ASGI E97055 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: ASGI | Statement: [Gunicorn, supportsInterface, ASGI]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ASGI
Context triple: [Gunicorn, supportsInterface, ASGI]
  • A. ASGI chosen
    ASGI (Asynchronous Server Gateway Interface) is a Python standard for asynchronous web servers and applications that enables high-performance, concurrent web frameworks and services.
  • B. WSGI
    WSGI (Web Server Gateway Interface) is a Python standard that defines a common interface between web servers and Python web applications or frameworks.
  • C. asgiref
    asgiref is a Python library that provides reference implementations and utilities for working with the ASGI (Asynchronous Server Gateway Interface) specification, commonly used in asynchronous web frameworks like Django and Starlette.
  • 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. 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_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_69b5c7237b608190ab5aca56027344c4 completed March 14, 2026, 8:37 p.m.
Created at: March 12, 2026, 11:07 p.m.