Triple

T10851811
Position Surface form Disambiguated ID Type / Status
Subject Python 3.8 E256163 entity
Predicate hasPEP P13854 FINISHED
Object PEP 572 E51179 NE FINISHED

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 572 | Statement: [Python 3.8, hasPEP, PEP 572]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PEP 572
Context triple: [Python 3.8, hasPEP, PEP 572]
  • A. PEP 572 chosen
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • B. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • C. PEP 695
    PEP 695 is a Python Enhancement Proposal that introduces a new, more concise syntax for type parameter declarations to improve the language’s support for generics and static typing.
  • 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 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: hasPEP
Context triple: [Python 3.8, hasPEP, PEP 572]
  • A. hasPar
    Indicates a relationship where one entity has another entity as its parent.
  • B. has chosen
    Indicates that one entity possesses, owns, contains, or includes another entity as part of its state or composition.
  • C. hasSept
    Indicates that one entity possesses, contains, or is associated with a sept (a subdivision or clan group) in relation to another entity.
  • D. hasPotential
    Indicates that an entity possesses the capacity or possibility to develop, achieve, or exhibit a particular state, quality, or outcome in the future.
  • E. hasApse
    Indicates that a structure or building possesses an apse, typically a semicircular or polygonal recess, as one of its architectural features.
  • F. None of above.

Provenance (4 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_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d75117b76c8190b0fb216b1428c3c7 completed April 9, 2026, 7:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69e154b2eb08819080a9905dbf378111 completed April 16, 2026, 9:29 p.m.
PD Predicate disambiguation batch_69d70d2b51448190bae748ed6c23edde completed April 9, 2026, 2:21 a.m.
Created at: April 8, 2026, 9:20 p.m.