Triple

T10782521
Position Surface form Disambiguated ID Type / Status
Subject Coquette E254361 entity
Predicate editor P1954 FINISHED
Object Daniel Mandell E295857 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: Daniel Mandell | Statement: [Coquette, editor, Daniel Mandell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Mandell
Context triple: [Coquette, editor, Daniel Mandell]
  • A. Daniel Mandell chosen
    Daniel Mandell was an American film editor renowned for his work on numerous classic Hollywood films and for winning multiple Academy Awards for Best Film Editing.
  • B. Jack Mandel
    Jack Mandel was a prominent philanthropist and businessman whose contributions to social work and education led to institutions such as the Jack, Joseph and Morton Mandel School of Applied Social Sciences being named in his honor.
  • C. Steven Baigelman
    Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
  • D. Dan Mindel
    Dan Mindel is a British cinematographer known for his work on major blockbuster films, including entries in the Star Trek and Star Wars franchises.
  • E. Michael D. Rosenthal
    Michael D. Rosenthal is a writer best known as the author whose work inspired the "Twilight Zone" episode "A Kind of Stopwatch."
  • 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_69d6aa609f008190a294200aefcb7bd5 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d732c5618081908f72838ed42ed05c completed April 9, 2026, 5:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3a9033148819095c6e7485c3a73e6 completed April 18, 2026, 3:53 p.m.
Created at: April 8, 2026, 9:17 p.m.