Triple
T18799794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PySide2 |
E459729
|
entity |
| Predicate | alternativeTo |
P5887
|
FINISHED |
| Object |
PyQt5
PyQt5 is a set of Python bindings for the Qt application framework, widely used to create cross-platform graphical user interfaces.
|
E459729
|
NE FINISHED |
How this triple was built (4 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 | Statement: [PySide2, alternativeTo, PyQt5]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PyQt5 Context triple: [PySide2, alternativeTo, PyQt5]
-
A.
PyQt5 or PySide2
PyQt5 or PySide2 are Python bindings for the Qt5 application framework, commonly used to create cross-platform graphical user interfaces.
-
B.
PySide2
PySide2 is the official Python binding for the Qt 5 application framework, enabling the creation of cross-platform graphical user interfaces.
-
C.
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.
-
D.
Tkinter
Tkinter is Python’s standard GUI toolkit, providing a simple interface to the Tk GUI library for building desktop applications.
-
E.
Qt
Qt is a cross-platform application development framework widely used for building graphical user interfaces and multi-platform software in C++.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PyQt5 Triple: [PySide2, alternativeTo, PyQt5]
Generated description
PyQt5 is a set of Python bindings for the Qt application framework, widely used to create cross-platform graphical user interfaces.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PyQt5 Target entity description: PyQt5 is a set of Python bindings for the Qt application framework, widely used to create cross-platform graphical user interfaces.
-
A.
PyQt5 or PySide2
chosen
PyQt5 or PySide2 are Python bindings for the Qt5 application framework, commonly used to create cross-platform graphical user interfaces.
-
B.
PySide2
PySide2 is the official Python binding for the Qt 5 application framework, enabling the creation of cross-platform graphical user interfaces.
-
C.
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.
-
D.
Tkinter
Tkinter is Python’s standard GUI toolkit, providing a simple interface to the Tk GUI library for building desktop applications.
-
E.
Qt
Qt is a cross-platform application development framework widely used for building graphical user interfaces and multi-platform software in C++.
- F. None of above.
Provenance (5 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. |
| NED1 | Entity disambiguation (via context triple) | batch_6a054723def4819097961a04087a54a3 |
completed | May 14, 2026, 3:53 a.m. |
| NEDg | Description generation | batch_6a0549358ba081909eb649898d1b3a8a |
completed | May 14, 2026, 4:01 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0549c825f481909d2da63688b36186 |
completed | May 14, 2026, 4:04 a.m. |
Created at: April 10, 2026, 11:53 a.m.