Triple

T17557260
Position Surface form Disambiguated ID Type / Status
Subject PEP 420 E427619 entity
Predicate relatedTo P37 FINISHED
Object PEP 273 NE NERFINISHED

How this triple was built (3 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 273 | Statement: [PEP 420, relatedTo, PEP 273]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PEP 273
Context triple: [PEP 420, relatedTo, PEP 273]
  • A. PEP 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • B. PEP 643
    PEP 643 is a Python Enhancement Proposal that defines a standardized way to specify and handle metadata for Python packages.
  • C. 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.
  • D. 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.
  • E. PEP 624
    PEP 624 is a Python Enhancement Proposal that specifies the removal of the Py_UNICODE encoder APIs from the CPython C API to streamline and modernize Unicode handling in Python.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: PEP 273
Target entity description: 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.
  • A. PEP 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • B. PEP 643
    PEP 643 is a Python Enhancement Proposal that defines a standardized way to specify and handle metadata for Python packages.
  • C. 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.
  • D. 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.
  • E. PEP 624
    PEP 624 is a Python Enhancement Proposal that specifies the removal of the Py_UNICODE encoder APIs from the CPython C API to streamline and modernize Unicode handling in Python.
  • F. None of above. chosen

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.