Triple

T15565795
Position Surface form Disambiguated ID Type / Status
Subject The Old Man and the Gun E371110 entity
Predicate musicBy P1952 FINISHED
Object Daniel Hart E254796 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 Hart | Statement: [The Old Man and the Gun, musicBy, Daniel Hart]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Daniel Hart
Context triple: [The Old Man and the Gun, musicBy, Daniel Hart]
  • A. Daniel Hart chosen
    Daniel Hart is an American composer and musician known for his evocative film scores and frequent collaborations with director David Lowery.
  • B. David Kraft
    David Kraft is a member of the prominent Kraft family, known for its significant influence in the American food industry and philanthropy.
  • C. David Hassinger
    David Hassinger was an American recording engineer and producer known for his work with prominent 1960s rock acts, including the Grateful Dead and the Electric Prunes.
  • D. Don Sebesky
    Don Sebesky was an American jazz trombonist, composer, and prolific arranger best known for his innovative orchestrations for major jazz and pop artists.
  • E. Dennis Marks
    Dennis Marks is a name shared by several notable individuals, including an American television producer and writer and a British opera director and translator.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04ddd753c8190b51eaef433258081 completed April 16, 2026, 2:47 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff5f30e48881908b30a71796fe0d72 completed May 9, 2026, 4:22 p.m.
Created at: April 10, 2026, 4:10 a.m.