Triple

T2417401
Position Surface form Disambiguated ID Type / Status
Subject PEP 695 E52336 entity
Predicate relatedTo P37 FINISHED
Object PEP 585
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.
 E265442 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 585 | Statement: [PEP 695, relatedTo, PEP 585]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 585
Context triple: [PEP 695, relatedTo, PEP 585]
  • A. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • B. 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.
  • C. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • D. 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.
  • 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: PEP 585
Triple: [PEP 695, relatedTo, PEP 585]
Generated description
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.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEP 585
Target entity description: 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.
  • A. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • B. 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.
  • C. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted in Python 3.10.
  • D. 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.
  • 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_69ab495622948190bc6bc6e4cddaf645 completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abc94eafd481909eeff689e5bf5960 completed March 7, 2026, 6:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69aebf4dcf6c8190a51f26af7e7a9b9c completed March 9, 2026, 12:38 p.m.
NEDg Description generation batch_69aec2b3291c8190966344cd20963660 completed March 9, 2026, 12:53 p.m.
NED2 Entity disambiguation (via description) batch_69aec30f9ef481909b83f3cf9fd6e998 completed March 9, 2026, 12:54 p.m.
Created at: March 6, 2026, 9:42 p.m.