Triple

T14752102
Position Surface form Disambiguated ID Type / Status
Subject Factory Girl E346634 entity
Predicate cinematographyBy 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: [Factory Girl, cinematographyBy, Michael Grady]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Grady
Context triple: [Factory Girl, cinematographyBy, 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. Gregory Hess
    Gregory Hess was an American protester whose conviction for disorderly conduct during an antiwar demonstration led to the landmark U.S. Supreme Court free speech case Hess v. Indiana.
  • C. Matthew Aldrich
    Matthew Aldrich is an American screenwriter best known for co-writing Pixar’s Academy Award–winning animated film "Coco."
  • D. 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.
  • E. Andrew Crawford
    Andrew Crawford is a relatively common personal name shared by multiple individuals across various professions, including sports, academia, and the arts.
  • 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_69d822e6f1c88190bc494d491a907114 completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69dec7d40efc8190bb1be34c19a2b57c completed April 14, 2026, 11:03 p.m.
NED1 Entity disambiguation (via context triple) batch_69fdfb9a56a08190b6a178cd930a072d completed May 8, 2026, 3:04 p.m.
Created at: April 10, 2026, 1:30 a.m.