Triple

T2301345
Position Surface form Disambiguated ID Type / Status
Subject PEP 622 E51738 entity
Predicate supersededBy P101 FINISHED
Object 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.
 E257816 NE FINISHED

How this triple was built (4 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 636 | Statement: [PEP 622, supersededBy, PEP 636]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 636
Context triple: [PEP 622, supersededBy, PEP 636]
  • A. PEP 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • B. PEP 635
    PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced 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 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • E. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: PEP 636
Triple: [PEP 622, supersededBy, PEP 636]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEP 636
Target entity description: 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.
  • A. PEP 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • B. PEP 635
    PEP 635 is a Python Enhancement Proposal that provides a detailed rationale and motivation for the structural pattern matching feature introduced 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 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • E. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • F. None of above. chosen

Provenance (5 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_69a88b0a9f248190bcff941463d8f65a completed March 4, 2026, 7:42 p.m.
NER Named-entity recognition batch_69abc5ef51948190ae828d8ee02feb75 completed March 7, 2026, 6:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae960ae538819095c66b91952800b7 completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae96d95c648190bca59f97c01e59fc completed March 9, 2026, 9:46 a.m.
NED2 Entity disambiguation (via description) batch_69ae974d062c819091459361f519ee3e completed March 9, 2026, 9:47 a.m.
Created at: March 4, 2026, 7:49 p.m.