Triple

T22002540
Position Surface form Disambiguated ID Type / Status
Subject Halloweentown High E543366 entity
Predicate cinematographyBy P1953 FINISHED
Object Michael Slovis NE NERFINISHED

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 Slovis | Statement: [Halloweentown High, cinematographyBy, Michael Slovis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Slovis
Context triple: [Halloweentown High, cinematographyBy, Michael Slovis]
  • A. Michael Slovis chosen
    Michael Slovis is an American cinematographer and television director known for his work on acclaimed series such as Breaking Bad and Game of Thrones.
  • B. Brent Sayers
    Brent Sayers is an American music executive and co-founder of the influential independent hip hop label Rhymesayers Entertainment.
  • C. Jeff Daugherty
    Jeff Daugherty is an American local government official who serves as the mayor of Lindale, Texas.
  • D. Doug Madsen
    Doug Madsen is a middle-aged suburban man and member of a motorcycle-riding friends group in the comedy film "Wild Hogs."
  • E. Darius N. Couch
    Darius N. Couch was a Union Army major general during the American Civil War, noted for his corps command in key Eastern Theater campaigns.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e2c814c8190837d072789000486 completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f1276bf2a48190910d9c27f1c5e74f completed April 28, 2026, 9:32 p.m.
Created at: April 16, 2026, 8:20 p.m.