Triple

T7952293
Position Surface form Disambiguated ID Type / Status
Subject Deep Valley E184644 entity
Predicate director P255 FINISHED
Object Jean Negulesco E224479 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: Jean Negulesco | Statement: [Deep Valley, director, Jean Negulesco]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jean Negulesco
Context triple: [Deep Valley, director, Jean Negulesco]
  • A. Jean Negulesco chosen
    Jean Negulesco was a Romanian-American film director and screenwriter best known for his stylish Hollywood dramas and romances from the 1940s and 1950s.
  • B. Gregory La Cava
    Gregory La Cava was an American film director best known for his sophisticated 1930s comedies and character-driven dramas in Hollywood’s Golden Age.
  • C. Ivan Goff
    Ivan Goff was an Australian-born screenwriter best known for his work in Hollywood film and television, including co-writing influential crime dramas and creating popular TV series.
  • D. Harold Chasen
    Harold Chasen is the morbid, death-obsessed young protagonist of the dark romantic comedy film "Harold and Maude."
  • E. Harold Hecht
    Harold Hecht was an American film producer and talent agent best known for co-founding Hecht-Hill-Lancaster and producing acclaimed mid-20th-century films such as "Marty."
  • 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_69ca8292cba881908a64427b938dac47 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3b5c95908190b6ee900d2b324574 completed March 31, 2026, 3:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe0530b8881908e35b10917e69899 completed March 31, 2026, 2:55 p.m.
Created at: March 30, 2026, 5:10 p.m.