Triple

T7049503
Position Surface form Disambiguated ID Type / Status
Subject How to Steal a Million E163727 entity
Predicate screenplayBy P15305 FINISHED
Object Harry Kurnitz E224186 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: Harry Kurnitz | Statement: [How to Steal a Million, screenplayBy, Harry Kurnitz]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Harry Kurnitz
Context triple: [How to Steal a Million, screenplayBy, Harry Kurnitz]
  • A. Harry Kurnitz chosen
    Harry Kurnitz was an American playwright, novelist, and screenwriter known for his witty crime and mystery scripts in mid-20th-century Hollywood.
  • B. Milton R. Krasner
    Milton R. Krasner was an American cinematographer renowned for his work on numerous classic Hollywood films, including the acclaimed drama "All About Eve."
  • C. Laurence Feininger
    Laurence Feininger was a musicologist and Roman Catholic priest known for his scholarly work on early sacred music and as the son of German-American painter Lyonel Feininger.
  • D. Robert Krasker
    Robert Krasker was an Australian-born cinematographer best known for his atmospheric black-and-white work on classic films such as "The Third Man."
  • E. Arthur Kober
    Arthur Kober was an American humorist, playwright, and screenwriter known for his witty short stories and work in Hollywood during 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_69c6885f598c8190b6b6495c59d8d962 completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6e24d5e8c8190b37e56107e6da8ab completed March 27, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69c802a126e081908ca3074b636764d1 completed March 28, 2026, 4:32 p.m.
Created at: March 27, 2026, 2:37 p.m.