Triple

T10851735
Position Surface form Disambiguated ID Type / Status
Subject Assignment Expressions E256161 entity
Predicate introducedInVersion P11235 FINISHED
Object Python 3.8 E256163 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: Python 3.8 | Statement: [Assignment Expressions, introducedInVersion, Python 3.8]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Python 3.8
Context triple: [Assignment Expressions, introducedInVersion, Python 3.8]
  • A. Python 3.8 chosen
    Python 3.8 is a major release of the Python programming language that introduced several new language features, performance improvements, and standard library enhancements.
  • B. Python 3.9
    Python 3.9 is a stable release of the Python programming language that introduced features like dictionary union operators, type hinting improvements, and new string methods before being succeeded by Python 3.10.
  • C. Python 3.10
    Python 3.10 is a major release of the Python programming language that introduced structural pattern matching and various syntax and performance improvements.
  • D. Python 3.11
    Python 3.11 is a major release of the Python programming language notable for significant performance improvements, enhanced error messages, and new language features such as exception groups and the `tomllib` module.
  • 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.

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_69d6aa81a5d08190aa86689061d1ddd2 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_69deb17d978c8190883b4a56e88859de completed April 14, 2026, 9:28 p.m.
Created at: April 8, 2026, 9:20 p.m.