Triple

T3930722
Position Surface form Disambiguated ID Type / Status
Subject Simon Emanuel E90786 entity
Predicate employer P7 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: [Simon Emanuel, employer, Lucasfilm]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lucasfilm
Context triple: [Simon Emanuel, employer, 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_69aed95f26e0819094b0e71974543a19 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeeda7cf3c81909df30744bddbad7e completed March 9, 2026, 3:56 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5288408f0819090217513e7a21091 completed March 14, 2026, 9:21 a.m.
Created at: March 9, 2026, 3:23 p.m.