Triple

T11970285
Position Surface form Disambiguated ID Type / Status
Subject David C. Evans E284900 entity
Predicate notableStudent P4838 FINISHED
Object John Warnock E10030 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: John Warnock | Statement: [David C. Evans, notableStudent, John Warnock]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Warnock
Context triple: [David C. Evans, notableStudent, John Warnock]
  • A. John Warnock chosen
    John Warnock was an American computer scientist and co-founder of Adobe Systems, best known for pioneering the PostScript language and the PDF file format.
  • B. Charles Geschke
    Charles Geschke was an American computer scientist and entrepreneur best known as the co-founder of Adobe Systems and a pioneer of desktop publishing technologies.
  • C. Jef Raskin
    Jef Raskin was a human–computer interface expert and computer scientist best known for initiating and leading the early development of Apple’s Macintosh project.
  • D. Victor Kilian
    Victor Kilian was an American character actor known for his prolific work in film and television from the 1920s through the 1970s.
  • E. Bill Buxton
    Bill Buxton is a pioneering computer scientist and designer known for his influential work in human-computer interaction, input technologies, and user experience design.
  • 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_69d6ab2eaeb881909f7914758f859413 completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d9037bee54819085242a3ef3e286f9 completed April 10, 2026, 2:04 p.m.
NED1 Entity disambiguation (via context triple) batch_69f459691ff0819099282172933d2d81 completed May 1, 2026, 7:42 a.m.
Created at: April 8, 2026, 9:46 p.m.