Triple

T10851819
Position Surface form Disambiguated ID Type / Status
Subject Python 3.8 E256163 entity
Predicate hasPEP P13854 FINISHED
Object PEP 590
PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
 E896510 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 590 | Statement: [Python 3.8, hasPEP, PEP 590]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEP 590
Context triple: [Python 3.8, hasPEP, PEP 590]
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 578
    PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • D. 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.
  • E. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted 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 590
Triple: [Python 3.8, hasPEP, PEP 590]
Generated description
PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEP 590
Target entity description: PEP 590 is the Python Enhancement Proposal that introduced the "vectorcall" protocol to speed up and standardize function calls in CPython.
  • A. PEP 570
    PEP 570 is the Python Enhancement Proposal that introduced positional-only parameters to Python function definitions, formalizing a syntax for arguments that must be passed by position.
  • B. PEP 572
    PEP 572 is the Python proposal that introduced the “walrus operator” (:=) for assignment expressions, allowing assignment within larger expressions.
  • C. PEP 578
    PEP 578 is a Python enhancement proposal that introduces a security audit hook framework to help monitor and control runtime events in Python applications.
  • D. 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.
  • E. PEP 622
    PEP 622 is a Python Enhancement Proposal that introduced the design for structural pattern matching syntax later adopted 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_69d6aa83d1448190a66d93c32394d21f 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_69e2d6c077608190822d66b23866f5eb completed April 18, 2026, 12:56 a.m.
NEDg Description generation batch_69e2ff1ddd2c8190b31f5007f7492a4e completed April 18, 2026, 3:48 a.m.
NED2 Entity disambiguation (via description) batch_69e3260494bc81909e3dd4829697fb72 completed April 18, 2026, 6:34 a.m.
Created at: April 8, 2026, 9:20 p.m.