Triple

T11988198
Position Surface form Disambiguated ID Type / Status
Subject Michael Abels E285337 entity
Predicate name P16 FINISHED
Object Michael Abels E285337 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 Abels | Statement: [Michael Abels, name, Michael Abels]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Abels
Context triple: [Michael Abels, name, Michael Abels]
  • A. Michael Abels chosen
    Michael Abels is an American composer best known for his innovative, genre-blending film scores for Jordan Peele’s movies, including Get Out, Us, and Nope.
  • B. Mychael Danna
    Mychael Danna is a Canadian film composer renowned for his evocative, often world-music-infused scores, including his Academy Award–winning work on "Life of Pi."
  • C. Michael Giacchino
    Michael Giacchino is an American composer renowned for his award-winning scores for films, television, and video games, including major franchises like Pixar, Marvel, and Star Trek.
  • D. Rupert Gregson-Williams
    Rupert Gregson-Williams is a British film and television composer known for scoring major Hollywood productions such as "Wonder Woman," "Aquaman," and "The Crown."
  • E. Bear McCreary
    Bear McCreary is an American composer best known for his dynamic and thematic scores for television series such as Battlestar Galactica, The Walking Dead, and Outlander, as well as films and video games.
  • 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_69d6ab44a77c8190a652f4b27164e4ef completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d903ae28708190a826bad1624343eb completed April 10, 2026, 2:05 p.m.
NED1 Entity disambiguation (via context triple) batch_69f48abcf2588190a44e6e31e045b356 completed May 1, 2026, 11:13 a.m.
Created at: April 8, 2026, 9:46 p.m.