Triple

T2125147
Position Surface form Disambiguated ID Type / Status
Subject John Lasseter E46406 entity
Predicate workedFor P1910 FINISHED
Object Lucasfilm E32166 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: Lucasfilm | Statement: [John Lasseter, workedFor, Lucasfilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lucasfilm
Context triple: [John Lasseter, workedFor, Lucasfilm]
  • A. Lucasfilm chosen
    Lucasfilm is a renowned American film and television production company best known for creating the Star Wars and Indiana Jones franchises.
  • B. Eon Productions
    Eon Productions is a British film production company best known for producing the long-running James Bond movie franchise.
  • C. Fantasy Studios
    Fantasy Studios was a renowned recording facility in Berkeley, California, known for hosting sessions by prominent rock, jazz, and film soundtrack artists.
  • D. Walt Disney Studios
    Walt Disney Studios is a major American film studio and entertainment company division of The Walt Disney Company, known for producing and distributing animated and live-action films worldwide.
  • E. Annapurna Studios
    Annapurna Studios is a prominent Indian film production and post-production company based in Hyderabad, widely recognized for its role in shaping Telugu cinema.
  • 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_69a88a1626548190ae59a5028c3baa8e completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abbb57bc6881909f04a407beff33a6 completed March 7, 2026, 5:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7ef2dda08190b55919f0df935a6d completed March 9, 2026, 8:04 a.m.
Created at: March 4, 2026, 7:44 p.m.