Triple

T15033468
Position Surface form Disambiguated ID Type / Status
Subject Brazil (1985 film) E378416 entity
Predicate musicBy P1952 FINISHED
Object Michael Kamen E291037 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: Michael Kamen | Statement: [Brazil (1985 film), musicBy, Michael Kamen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kamen
Context triple: [Brazil (1985 film), musicBy, Michael Kamen]
  • A. Michael Kamen chosen
    Michael Kamen was an American composer and conductor renowned for his film and television scores, including major works in action cinema and acclaimed historical dramas.
  • B. Ron Goodwin
    Ron Goodwin was a British composer and conductor best known for his rousing film scores for war and adventure movies in the mid-20th century.
  • C. Albert Weinert
    Albert Weinert was a German-American sculptor and monument designer known for his public memorials in the United States.
  • D. Christophe Beck
    Christophe Beck is a Canadian composer best known for his film and television scores, including work on projects like "Buffy the Vampire Slayer" and various major Hollywood films.
  • E. Elliot Goldenthal
    Elliot Goldenthal is an American composer known for his innovative, often experimental film scores for movies such as "Interview with the Vampire," "Batman Forever," and "Frida."
  • 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_69d85cd46b2c819090d054c27787f677 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded7e3a7c8819081f26c2435c1bcb2 completed April 15, 2026, 12:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69fee5e0ed148190bd357be922fe8319 completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 2:59 a.m.