Triple

T11215872
Position Surface form Disambiguated ID Type / Status
Subject Python generic types E265438 entity
Predicate extendedIn P49342 FINISHED
Object PEP 585 E265442 NE FINISHED

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 585 | Statement: [Python generic types, extendedIn, PEP 585]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 585
Context triple: [Python generic types, extendedIn, PEP 585]
  • A. PEP 585 chosen
    PEP 585 is a Python Enhancement Proposal that introduced built-in generic types (like list[int] and dict[str, int]) as a modern replacement for many typing module aliases.
  • B. PEP 578
    PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • C. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • D. PEP 618
    PEP 618 is a Python Enhancement Proposal that introduced the `strict` parameter to the built-in `zip` function, enabling stricter handling of iterables with mismatched lengths.
  • E. 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.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8e8eef48190932a85784ce15c86 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e49762e3188190ba3c0e01cf04f6a1 completed April 19, 2026, 8:50 a.m.
Created at: April 8, 2026, 9:30 p.m.