Triple
T18799790
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PySide2 |
E459729
|
entity |
| Predicate | hasModule |
P12988
|
FINISHED |
| Object | PySide2.QtWebEngineWidgets |
—
|
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: PySide2.QtWebEngineWidgets | Statement: [PySide2, hasModule, PySide2.QtWebEngineWidgets]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PySide2.QtWebEngineWidgets Context triple: [PySide2, hasModule, PySide2.QtWebEngineWidgets]
-
A.
Qt WebEngine
chosen
Qt WebEngine is a Qt framework module that embeds a Chromium-based web browser engine to enable rendering and interaction with web content in Qt applications.
-
B.
PySide2
PySide2 is the official Python binding for the Qt 5 application framework, enabling the creation of cross-platform graphical user interfaces.
-
C.
PyQt5 or PySide2
PyQt5 or PySide2 are Python bindings for the Qt5 application framework, commonly used to create cross-platform graphical user interfaces.
-
D.
QtWebKit
QtWebKit is a port of the WebKit browser engine that integrates it with the Qt application framework for embedding web content in Qt-based applications.
-
E.
PyQt5.QtWidgets
PyQt5.QtWidgets is the PyQt5 submodule that provides the core set of GUI widget classes and related functionality for building desktop applications with Qt in Python.
- 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.