Triple

T400399
Position Surface form Disambiguated ID Type / Status
Subject Python Enhancement Proposals E9267 entity
Predicate abbreviation P43 FINISHED
Object PEPs
PEPs are formal design documents that propose and describe new features, processes, or changes for the Python programming language and its community.
 E51003 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: PEPs | Statement: [Python Enhancement Proposals, abbreviation, PEPs]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: PEPs
Context triple: [Python Enhancement Proposals, abbreviation, PEPs]
  • A. Pep
    Pep is the widely used nickname of Josep "Pep" Guardiola, the renowned Spanish football manager and former player.
  • B. PE
    PE is the two-letter ISO 3166-1 alpha-2 country code assigned to Peru for international standardization and referencing.
  • C. BEP
    BEP is a United States government agency responsible for designing and producing paper currency and other secure documents.
  • D. EOP
    EOP is the collective group of offices and agencies that directly support the President of the United States in carrying out executive responsibilities and policy initiatives.
  • E. PEG
    PEG is the stock ticker symbol for Public Service Enterprise Group, a major U.S. energy company primarily involved in regulated electric and gas utility operations and power generation.
  • 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: PEPs
Triple: [Python Enhancement Proposals, abbreviation, PEPs]
Generated description
PEPs are formal design documents that propose and describe new features, processes, or changes for the Python programming language and its community.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: PEPs
Target entity description: PEPs are formal design documents that propose and describe new features, processes, or changes for the Python programming language and its community.
  • A. Pep
    Pep is the widely used nickname of Josep "Pep" Guardiola, the renowned Spanish football manager and former player.
  • B. PE
    PE is the two-letter ISO 3166-1 alpha-2 country code assigned to Peru for international standardization and referencing.
  • C. BEP
    BEP is a United States government agency responsible for designing and producing paper currency and other secure documents.
  • D. EOP
    EOP is the collective group of offices and agencies that directly support the President of the United States in carrying out executive responsibilities and policy initiatives.
  • E. PEG
    PEG is the stock ticker symbol for Public Service Enterprise Group, a major U.S. energy company primarily involved in regulated electric and gas utility operations and power generation.
  • 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_69a2e8004cb88190b92ed1add6abf41a completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2ec8e655c819081eff85c0ef55fa5 completed Feb. 28, 2026, 1:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4103f9f588190aabdf7f5d6422d09 completed March 1, 2026, 10:09 a.m.
NEDg Description generation batch_69a410e5ba148190a7fe0ee9861fb334 completed March 1, 2026, 10:11 a.m.
NED2 Entity disambiguation (via description) batch_69a41180ead48190a5edd2e66da413ba completed March 1, 2026, 10:14 a.m.
Created at: Feb. 28, 2026, 1:08 p.m.