Triple
T2313437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Python core developers |
E51008
|
entity |
| Predicate | governedBy |
P46
|
FINISHED |
| Object | PEP 13 |
E53026
|
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: PEP 13 | Statement: [Python core developers, governedBy, PEP 13]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 13 Context triple: [Python core developers, governedBy, PEP 13]
-
A.
PEP 13
chosen
PEP 13 is the Python Enhancement Proposal that defines the process and rules for selecting and operating the Python Steering Council, the core governance body of the Python project.
-
B.
PEP 635
PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced in Python 3.10.
-
C.
PEP 634
PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
-
D.
PEP 636
PEP 636 is a Python Enhancement Proposal that serves as a tutorial-style guide to the structural pattern matching feature introduced in Python 3.10.
-
E.
PEP 1
PEP 1 is the foundational Python Enhancement Proposal that defines the purpose, structure, and workflow for all other PEPs in the Python community process.
- 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_69a88b074b908190ae983dbca7757d88 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc61c1ef08190911d5f58c2e91189 |
completed | March 7, 2026, 6:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3bbea88819089f069be4d369692 |
completed | March 9, 2026, 11:49 a.m. |
Created at: March 4, 2026, 7:49 p.m.