Triple

T4521739
Position Surface form Disambiguated ID Type / Status
Subject Friends with Benefits E103284 entity
Predicate cinematography P1953 FINISHED
Object Michael Grady E378827 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 Grady | Statement: [Friends with Benefits, cinematography, Michael Grady]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Grady
Context triple: [Friends with Benefits, cinematography, Michael Grady]
  • A. Michael Grady chosen
    Michael Grady is a cinematographer known for his work on feature films including the biographical legal drama "On the Basis of Sex."
  • B. Matthew Aldrich
    Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
  • C. Michael Graydon
    Michael Graydon is a retired senior Royal Air Force officer who served as a leading commander of British fighter aviation during the late 20th century.
  • D. Gregory Jarvis
    Gregory Jarvis was an American engineer and astronaut who served as a payload specialist on the ill-fated Space Shuttle Challenger mission STS-51-L.
  • E. Vincent Gaddis
    Vincent Gaddis was an American writer and researcher best known for coining and popularizing the modern mystery surrounding the Bermuda Triangle in the mid-20th century.
  • 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_69bd43dba59881908cf59b31df8c7ae1 completed March 20, 2026, 12:55 p.m.
NER Named-entity recognition batch_69bd574bd6908190b939d92b5809b101 completed March 20, 2026, 2:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69bda43a82c08190b3d43efcee45a8ea completed March 20, 2026, 7:47 p.m.
Created at: March 20, 2026, 1:02 p.m.