Triple

T18050996
Position Surface form Disambiguated ID Type / Status
Subject typing (Python module) E431925 entity
Predicate relatedPEP P37 FINISHED
Object PEP 560 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 560 | Statement: [typing (Python module), relatedPEP, PEP 560]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PEP 560
Context triple: [typing (Python module), relatedPEP, PEP 560]
  • A. PEP 560 chosen
    PEP 560 is a Python Enhancement Proposal that optimizes and refines the implementation of typing and generic types in Python, improving performance and simplifying the internal mechanics of the typing module.
  • B. PEP 590
    PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
  • C. PEP 566
    PEP 566 is a Python Enhancement Proposal that defines a standardized, extensible metadata format for Python packages to improve distribution and tooling interoperability.
  • D. PEP 552
    PEP 552 is a Python Enhancement Proposal that introduced deterministic, hash-based .pyc files to improve reproducibility and caching behavior in Python.
  • E. PEP 621
    PEP 621 is a Python Enhancement Proposal that standardizes how project metadata is declared in pyproject.toml, simplifying and unifying package configuration for Python packaging tools.
  • 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.