Triple

T4277982
Position Surface form Disambiguated ID Type / Status
Subject pip E97086 entity
Predicate implements P1417 FINISHED
Object PEP 440
PEP 440 is the Python Packaging Authority’s standard that defines a consistent versioning scheme for Python packages.
E427615 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 440 | Statement: [pip, implements, PEP 440]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: PEP 440
Context triple: [pip, implements, PEP 440]
  • 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 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 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 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. 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 440
Triple: [pip, implements, PEP 440]
Generated description
PEP 440 is the Python Packaging Authority’s standard that defines a consistent versioning scheme for Python packages.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: PEP 440
Target entity description: PEP 440 is the Python Packaging Authority’s standard that defines a consistent versioning scheme for Python packages.
  • A. PEP 508
    PEP 508 is a Python Enhancement Proposal that defines the standard syntax for specifying package dependencies and environment markers in Python packaging.
  • B. PEP 503
    PEP 503 is a Python Enhancement Proposal that defines the simple repository API used by package installers like pip to discover and download Python packages.
  • C. PEP 425 platform compatibility tags
    PEP 425 platform compatibility tags are standardized identifiers used in Python packaging to specify which operating systems, architectures, and Python versions a built distribution (like a wheel) is compatible with.
  • D. PEP 634
    PEP 634 is the Python Enhancement Proposal that formally specifies the semantics of structural pattern matching introduced in Python 3.10.
  • 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_69b34544be3c819084d1ab82d29f90c5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3501ef1388190b0c968b069014a59 completed March 12, 2026, 11:45 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5b7b708b481908c1683741f84ee55 completed March 14, 2026, 7:32 p.m.
NEDg Description generation batch_69b5bb84ed808190891f2a75296c11c6 completed March 14, 2026, 7:48 p.m.
NED2 Entity disambiguation (via description) batch_69b5bc392228819089fe64b55bb572cc completed March 14, 2026, 7:51 p.m.
Created at: March 12, 2026, 11:07 p.m.