Triple

T4521507
Position Surface form Disambiguated ID Type / Status
Subject SexyBack E103279 entity
Predicate musicVideoDirector P4911 FINISHED
Object Michael Haussman E414296 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 Haussman | Statement: [SexyBack, musicVideoDirector, Michael Haussman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Haussman
Context triple: [SexyBack, musicVideoDirector, Michael Haussman]
  • A. Michael Haussman chosen
    Michael Haussman is an American director and filmmaker best known for his work on high-profile music videos and commercials.
  • B. Michael Hausman
    Michael Hausman is an American film producer and production manager known for his work on acclaimed films such as "Silkwood," "Amadeus," and "Gangs of New York."
  • C. Michael Filerman
    Michael Filerman was an American television producer best known for developing and producing popular prime-time soap operas during the 1970s and 1980s.
  • D. Michael Hecht
    Michael Hecht is the birth name of Michael Howard, a British Conservative politician who served as Leader of the Opposition and Home Secretary.
  • E. Michael Wincott
    Michael Wincott is a Canadian character actor known for his distinctive raspy voice and memorable villainous roles in films such as The Crow, Robin Hood: Prince of Thieves, and Nope.
  • 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_69bf32f31ecc81909cb29b762481b08e completed March 22, 2026, 12:08 a.m.
Created at: March 20, 2026, 1:02 p.m.