Triple

T19756074
Position Surface form Disambiguated ID Type / Status
Subject James J. Gibson E474502 entity
Predicate influenced P9 FINISHED
Object Don Norman NE NERFINISHED

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: Don Norman | Statement: [James J. Gibson, influenced, Don Norman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Don Norman
Context triple: [James J. Gibson, influenced, Don Norman]
  • A. Donald A. Norman chosen
    Donald A. Norman is a cognitive scientist and design theorist best known for his influential work on user-centered design and the psychology of everyday objects.
  • B. Ben Shneiderman
    Ben Shneiderman is a pioneering computer scientist and human-computer interaction researcher known for foundational work on user interface design and information visualization.
  • C. Jakob Nielsen
    Jakob Nielsen is a Danish web usability consultant and human–computer interaction researcher, widely known for pioneering usability heuristics and co-founding the Nielsen Norman Group.
  • D. 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.
  • E. Brian Leveson
    Brian Leveson is a British judge best known for leading the public inquiry into the culture, practices, and ethics of the UK press following the phone-hacking scandal.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8e51940a0819087bd2996f98da668 completed April 10, 2026, 11:55 a.m.
NER Named-entity recognition batch_69e6531afcbc8190bd5364700008f6d8 completed April 20, 2026, 4:23 p.m.
Created at: April 10, 2026, 1:48 p.m.