Triple

T1432098
Position Surface form Disambiguated ID Type / Status
Subject You Know My Name E30469 entity
Predicate hasWriter P4244 FINISHED
Object David Arnold E175431 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: David Arnold | Statement: [You Know My Name, hasWriter, David Arnold]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: David Arnold
Context triple: [You Know My Name, hasWriter, David Arnold]
  • A. David Arnold chosen
    David Arnold is a British film composer best known for his work on several James Bond soundtracks and other major Hollywood films.
  • B. David Gest
    David Gest was an American television personality and music producer best known for his high-profile marriage to entertainer Liza Minnelli and his appearances on British reality TV.
  • C. Steven Pemberton
    Steven Pemberton is a British computer scientist and software engineer known for his work on programming languages, web standards, and contributions to the development of ABC and early Python influences.
  • D. Alan Webber
    Alan Webber is an American politician and former business magazine co-founder who serves as the mayor of Santa Fe, New Mexico.
  • E. Phil Woolpert
    Phil Woolpert was a prominent American college basketball coach best known for leading the University of San Francisco to multiple national championships in the 1950s.
  • 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_69a498fc69ec8190b61722bd4b67c4d2 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c9df014081908a6e2f41ba012ecc completed March 1, 2026, 11:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ad36fd91488190b0431bc1c83c64e3 completed March 8, 2026, 8:44 a.m.
Created at: March 1, 2026, 8 p.m.