Triple

T23477176
Position Surface form Disambiguated ID Type / Status
Subject Police Academy E570294 entity
Predicate cinematographyBy P1953 FINISHED
Object Michael D. Margulies 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 D. Margulies | Statement: [Police Academy, cinematographyBy, Michael D. Margulies]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael D. Margulies
Context triple: [Police Academy, cinematographyBy, Michael D. Margulies]
  • A. Michael D. Margulies chosen
    Michael D. Margulies was an American cinematographer known for his work on 1970s and 1980s films and television productions.
  • B. Martin Margulies
    Martin Margulies is an American real estate developer and prominent contemporary art collector best known for the Margulies Collection at the Warehouse in Miami.
  • C. Michael Levin
    Michael Levin is a philosopher known for his work in epistemology, philosophy of race, and his controversial conservative views on social and political issues.
  • D. Michael A. Levine
    Michael A. Levine is an American composer and producer known for his work on film and television scores, including contributions to major Hollywood action movies.
  • E. William Margulies
    William Margulies was an American cinematographer known for his work on mid-20th-century Hollywood films and television productions.
  • 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_69e245af8a88819084f2704f6d265a92 completed April 17, 2026, 2:37 p.m.
NER Named-entity recognition batch_69f1a74dbea8819085ca84391039e7f7 completed April 29, 2026, 6:38 a.m.
Created at: April 17, 2026, 6:01 p.m.