Triple

T13744508
Position Surface form Disambiguated ID Type / Status
Subject Law Abiding Citizen E330177 entity
Predicate cinematographer P1953 FINISHED
Object Jonathan Sela E219932 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: Jonathan Sela | Statement: [Law Abiding Citizen, cinematographer, Jonathan Sela]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jonathan Sela
Context triple: [Law Abiding Citizen, cinematographer, Jonathan Sela]
  • A. Jonathan Sela chosen
    Jonathan Sela is a cinematographer known for his dynamic, high-energy visual work on major action films and thrillers.
  • B. Jonathan Benassaya
    Jonathan Benassaya is a French entrepreneur best known for co-founding the music streaming service Deezer.
  • C. Joshua Sternin
    Joshua Sternin is an American television writer and producer known for his work on animated and live-action series, including co-creating the 2012 Teenage Mutant Ninja Turtles TV adaptation.
  • D. Jonathan Teplitzky
    Jonathan Teplitzky is an Australian film director known for character-driven dramas such as "The Railway Man" and "Burning Man."
  • E. Joshua Bilenker
    Joshua Bilenker is an American oncologist and biotech executive best known for founding and leading Loxo Oncology, a precision medicine company focused on targeted cancer therapies.
  • 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_69d81c573f288190aa2403d484fa3d49 completed April 9, 2026, 9:38 p.m.
NER Named-entity recognition batch_69de0211ba5481909fbd5b447e3d5a02 completed April 14, 2026, 9 a.m.
NED1 Entity disambiguation (via context triple) batch_69f7b8cebcd481909ba75eef518b34c7 completed May 3, 2026, 9:06 p.m.
Created at: April 9, 2026, 10:08 p.m.