Triple
T17557094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | PEP 440 |
E427615
|
entity |
| Predicate | relatedTo |
P37
|
FINISHED |
| Object | PEP 427 |
—
|
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: PEP 427 | Statement: [PEP 440, relatedTo, PEP 427]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PEP 427 Context triple: [PEP 440, relatedTo, PEP 427]
-
A.
PEP 427
chosen
PEP 427 is the Python Enhancement Proposal that defines the Wheel binary package format used for distributing and installing Python projects.
-
B.
PEP 426
PEP 426 was a proposed Python Enhancement Proposal that aimed to standardize a new metadata format for Python packages but was ultimately superseded before full adoption.
-
C.
PEP 425
PEP 425 is a Python Enhancement Proposal that defines the standardized “compatibility tag” scheme used to identify which Python interpreter and platform a binary distribution (like a wheel) is compatible with.
-
D.
PEP 3107
PEP 3107 is the Python Enhancement Proposal that introduced function annotations, providing a standardized syntax for attaching metadata such as type information to function parameters and return values.
-
E.
PEP 273
PEP 273 is a Python Enhancement Proposal that introduced support for importing modules from ZIP archives, enabling Python code to be distributed and executed directly from compressed files.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4562413d08190acaa5272046d3626 |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 5:50 a.m.