Triple

T10915752
Position Surface form Disambiguated ID Type / Status
Subject PEP 636 E257816 entity
Predicate standardizedIn P7508 FINISHED
Object Python 3.10 language specification E888829 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 3.10 language specification | Statement: [PEP 636, standardizedIn, Python 3.10 language specification]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Python 3.10 language specification
Context triple: [PEP 636, standardizedIn, Python 3.10 language specification]
  • A. Python 3 language specification chosen
    The Python 3 language specification is the formal, authoritative document that defines the syntax, semantics, and core behavior of the Python 3 programming language.
  • B. 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.
  • C. 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.
  • D. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • E. 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.
  • 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_69d6aa864ed88190818280ab6791d065 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d77074c77c8190af91369eee11f1b7 completed April 9, 2026, 9:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69e216eb77dc81908c380f5fcd507275 completed April 17, 2026, 11:18 a.m.
Created at: April 8, 2026, 9:22 p.m.