Triple

T4441189
Position Surface form Disambiguated ID Type / Status
Subject Eraser E95773 entity
Predicate editedBy 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: [Eraser, editedBy, Michael Tronick]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Tronick
Context triple: [Eraser, editedBy, 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. 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.
  • C. Jan D. Achenbach
    Jan D. Achenbach was a prominent engineer and applied mechanician known for his pioneering contributions to wave propagation in solids, fracture mechanics, and nondestructive evaluation.
  • D. James S. Langer
    James S. Langer is an American theoretical physicist known for his work on phase transitions, pattern formation, and the dynamics of nonequilibrium systems.
  • E. Daniel Stern
    Daniel Stern is an American actor and director best known for his comedic roles in films like "City Slickers" and the "Home Alone" series, as well as for narrating the television show "The Wonder Years."
  • 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_69b3453ea2b48190a26f154b3b8fece5 completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b355ad71588190b1dcad4250472c29 completed March 13, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69b61380fca08190bf036a7d82cee0e7 completed March 15, 2026, 2:03 a.m.
Created at: March 12, 2026, 11:32 p.m.