Triple

T11216020
Position Surface form Disambiguated ID Type / Status
Subject PEP 484 E265441 entity
Predicate createdBy P806 FINISHED
Object Guido van Rossum E1899 NE FINISHED

How this triple was built (2 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: Guido van Rossum | Statement: [PEP 484, createdBy, Guido van Rossum]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Guido van Rossum
Context triple: [PEP 484, createdBy, Guido van Rossum]
  • A. Guido van Rossum chosen
    Guido van Rossum is a Dutch programmer best known as the creator of the Python programming language.
  • B. Johannes van Rossum
    Johannes van Rossum was a Dutch coachman and later companion closely associated with Princess Marianne of the Netherlands, with whom he had a long-term, controversial relationship.
  • C. Nick Coghlan
    Nick Coghlan is a prominent Python core developer and software engineer known for his influential work on Python’s governance, documentation, and language design.
  • D. Robert Kern
    Robert Kern was an American film editor active during Hollywood’s classic studio era, known for his work on numerous prominent MGM productions.
  • E. David Flanagan
    David Flanagan is a software developer and technical author best known for his widely used programming books, including "JavaScript: The Definitive Guide."
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69d6aac59460819089b9848b27f57848 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8e8eef48190932a85784ce15c86 completed April 9, 2026, 5:59 p.m.
NED1 Entity disambiguation (via context triple) batch_69e49762e3188190ba3c0e01cf04f6a1 completed April 19, 2026, 8:50 a.m.
Created at: April 8, 2026, 9:30 p.m.