Triple
T18799749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PyQt5 |
E459729
|
entity |
| Predicate | hasModule |
P12988
|
FINISHED |
| Object | PyQt5.QtSql |
—
|
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: PyQt5.QtSql | Statement: [PyQt5, hasModule, PyQt5.QtSql]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyQt5.QtSql Context triple: [PyQt5, hasModule, PyQt5.QtSql]
-
A.
Qt SQL
chosen
Qt SQL is a Qt framework module that provides a unified, high-level API for accessing and manipulating SQL databases across multiple backends.
-
B.
PyQt5 or PySide2
PyQt5 or PySide2 are Python bindings for the Qt5 application framework, commonly used to create cross-platform graphical user interfaces.
-
C.
SQLAlchemy
SQLAlchemy is a powerful Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level, flexible interface for working with relational databases.
-
D.
SQLite
SQLite is a lightweight, self-contained, serverless SQL database engine widely embedded in applications, operating systems, and devices.
-
E.
PySide2
PySide2 is the official Python binding for the Qt 5 application framework, enabling the creation of cross-platform graphical user interfaces.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02273b481909bc250144a0ace32 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.