Triple
T18799786
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PySide2 |
E459729
|
entity |
| Predicate | hasModule |
P12988
|
FINISHED |
| Object | PySide2.QtNetwork |
—
|
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.QtNetwork | Statement: [PySide2, hasModule, PySide2.QtNetwork]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PySide2.QtNetwork Context triple: [PySide2, hasModule, PySide2.QtNetwork]
-
A.
PySide2
chosen
PySide2 is the official Python binding for the Qt 5 application framework, enabling the creation of cross-platform graphical user interfaces.
-
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.
Qt WebEngine
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.
-
D.
Qt
Qt is a cross-platform application development framework widely used for building graphical user interfaces and multi-platform software in C++.
-
E.
Qt5Agg
Qt5Agg is a Matplotlib rendering backend that combines the Qt5 GUI framework with the Anti-Grain Geometry (Agg) engine to display high-quality interactive plots.
- 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.