Triple

T13602912
Position Surface form Disambiguated ID Type / Status
Subject Meet Joe Black E324987 entity
Predicate editor P1954 FINISHED
Object Michael Tronick E349918 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: Michael Tronick | Statement: [Meet Joe Black, editor, Michael Tronick]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Tronick
Context triple: [Meet Joe Black, editor, Michael Tronick]
  • A. Michael Tronick chosen
    Michael Tronick is an American film editor known for his work on numerous major Hollywood productions across several decades.
  • B. Lee M. E. Morin
    Lee M. E. Morin is a U.S. Navy physician and NASA astronaut who flew aboard Space Shuttle Atlantis on the STS-110 mission to the International Space Station.
  • C. Jerome Kagan
    Jerome Kagan was an influential American psychologist and pioneer in developmental psychology, best known for his research on temperament in children and its impact on later behavior.
  • D. Ann Belsky
    Ann Belsky was a costume designer and the late wife of Canadian actor Rick Moranis.
  • E. Michael Tomasello
    Michael Tomasello is an American developmental and comparative psychologist known for his influential research on child language acquisition, social cognition, and the evolution of human cooperation.
  • 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_69d80769eaf081909d82f44e484d6113 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb07ca07481909c45da551ea61ab4 completed April 12, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f93ec588190993baec788d22670 completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:49 p.m.