Triple
T10818191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 635 |
E255288
|
entity |
| Predicate | belongsTo |
P35
|
FINISHED |
| Object | Python PEP index |
E884134
|
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: Python PEP index | Statement: [PEP 635, belongsTo, Python PEP index]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Python PEP index Context triple: [PEP 635, belongsTo, Python PEP index]
-
A.
Python PEP index
chosen
The Python PEP index is the central, organized listing of all Python Enhancement Proposals, serving as the primary reference for the language’s design, standards, and evolution.
-
B.
Python Package Index
The Python Package Index (PyPI) is the central online repository where developers publish and download open-source Python software packages.
-
C.
PEP 1 – PEP Purpose and Guidelines
PEP 1 – PEP Purpose and Guidelines is the foundational Python Enhancement Proposal that defines the goals, structure, and workflow for all other PEPs in the Python development process.
-
D.
PEP 503
PEP 503 is a Python Enhancement Proposal that defines the simple repository API used by package installers like pip to discover and download Python packages.
-
E.
PEP 622
PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
- 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_69d6aa8081448190a9324184f2bd1c26 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7344866f88190be4addb7c8020fce |
completed | April 9, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69de855799748190b51745a198daa8d0 |
completed | April 14, 2026, 6:20 p.m. |
Created at: April 8, 2026, 9:18 p.m.