Triple

T18168710
Position Surface form Disambiguated ID Type / Status
Subject Meet John Doe E434962 entity
Predicate editedBy P1954 FINISHED
Object Daniel Mandell 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: Daniel Mandell | Statement: [Meet John Doe, editedBy, Daniel Mandell]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Mandell
Context triple: [Meet John Doe, editedBy, 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. Allan Mandelbaum
    Allan Mandelbaum is a film producer known for his work on projects such as the 2018 movie "Welcome Home."
  • C. 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.
  • D. Daniel Blumberg
    Daniel Blumberg is a British musician and composer known for his experimental work in indie rock and film scores.
  • E. Steven Baigelman
    Steven Baigelman is an American screenwriter and producer known for his work on biographical and crime dramas in film and television.
  • 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_69d8b90b7a188190b3fc7b8d4a6cd20a completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e4df53ef148190a32aad0253547645 completed April 19, 2026, 1:57 p.m.
Created at: April 10, 2026, 10:30 a.m.