Triple

T18050993
Position Surface form Disambiguated ID Type / Status
Subject typing (Python module) E431925 entity
Predicate relatedPEP P37 FINISHED
Object PEP 483 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 483 | Statement: [typing (Python module), relatedPEP, PEP 483]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PEP 483
Context triple: [typing (Python module), relatedPEP, PEP 483]
  • A. PEP 483 chosen
    PEP 483 is a Python Enhancement Proposal that lays out the theoretical foundations and design principles for Python’s type hinting and generic types system.
  • B. PEP 406
    PEP 406 is a Python Enhancement Proposal that explored a standardized virtual environment mechanism for Python, influencing later work on packaging and environment management.
  • C. 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Python Enhancement Proposal that aimed to standardize a new metadata format for Python packages but was ultimately superseded before full adoption.
  • D. PEP 582
    PEP 582 is a Python enhancement proposal that introduces a local `__pypackages__` directory for managing project-specific dependencies without using virtual environments.
  • E. 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.
  • 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_69d8b906482481908183315b9ecf9994 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4bff57ea08190a30a87993f7d3299 completed April 19, 2026, 11:43 a.m.
Created at: April 10, 2026, 10:25 a.m.