Triple

T1637374
Position Surface form Disambiguated ID Type / Status
Subject Welcome to Marwen E35386 entity
Predicate musicBy P1952 FINISHED
Object Alan Silvestri E18968 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: Alan Silvestri | Statement: [Welcome to Marwen, musicBy, Alan Silvestri]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alan Silvestri
Context triple: [Welcome to Marwen, musicBy, Alan Silvestri]
  • A. Alan Silvestri chosen
    Alan Silvestri is an American composer best known for his prolific film scores, including iconic work on movies such as the Back to the Future trilogy and numerous Marvel Cinematic Universe films.
  • B. Dave Silvestri
    Dave Silvestri is a former professional baseball infielder who played in Major League Baseball during the 1990s.
  • C. John Debney
    John Debney is an American film composer known for scoring a wide range of movies and television shows, including major studio productions and acclaimed dramas.
  • D. John Williams
    John Williams is an acclaimed American composer and conductor best known for his iconic film scores for franchises such as Star Wars, Indiana Jones, Harry Potter, and many others.
  • E. James Horner
    James Horner was an Academy Award–winning American film composer renowned for his emotionally powerful scores for movies such as Titanic, Braveheart, and A Beautiful Mind.
  • 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_69a886036bc081909ff5de16dbe5e8ea completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a90a192d588190bbfa4693ed787c05 completed March 5, 2026, 4:44 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae0aaf7a008190bf7e5808838c6c2b completed March 8, 2026, 11:47 p.m.
Created at: March 4, 2026, 7:28 p.m.