Triple

T2313332
Position Surface form Disambiguated ID Type / Status
Subject PEP 0 E51006 entity
Predicate hasSection P35 FINISHED
Object Standards Track PEPs
Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
 E258606 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: Standards Track PEPs | Statement: [PEP 0, hasSection, Standards Track PEPs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Standards Track PEPs
Context triple: [PEP 0, hasSection, Standards Track PEPs]
  • A. PEP 1 – PEP Purpose and Guidelines
    PEP 1 – PEP Purpose and Guidelines is the foundational Python Enhancement Proposal that defines the goals, structure, and workflow for all other PEPs in the Python development process.
  • 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 13: Python Language Governance
    PEP 13: Python Language Governance is the Python Enhancement Proposal that defines the structure, responsibilities, and election process of the Python Steering Council, establishing the project's formal governance model.
  • 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. 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: Standards Track PEPs
Triple: [PEP 0, hasSection, Standards Track PEPs]
Generated description
Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Standards Track PEPs
Target entity description: Standards Track PEPs are Python Enhancement Proposals that introduce or change core Python features, syntax, or standard library behavior and, once accepted, are intended to be implemented in the language.
  • A. PEP 1 – PEP Purpose and Guidelines
    PEP 1 – PEP Purpose and Guidelines is the foundational Python Enhancement Proposal that defines the goals, structure, and workflow for all other PEPs in the Python development process.
  • 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 13: Python Language Governance
    PEP 13: Python Language Governance is the Python Enhancement Proposal that defines the structure, responsibilities, and election process of the Python Steering Council, establishing the project's formal governance model.
  • 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. 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_69a88b074b908190ae983dbca7757d88 completed March 4, 2026, 7:41 p.m.
NER Named-entity recognition batch_69abc61c1ef08190911d5f58c2e91189 completed March 7, 2026, 6:30 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae9610b420819095afb76347ddf9ee completed March 9, 2026, 9:42 a.m.
NEDg Description generation batch_69ae97406c588190855ecb6c9ed00bc9 completed March 9, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ae97c82e8c8190a613444ea4ffb5a0 completed March 9, 2026, 9:50 a.m.
Created at: March 4, 2026, 7:49 p.m.