Triple

T1982449
Position Surface form Disambiguated ID Type / Status
Subject La La Land E43057 entity
Predicate stars P1956 FINISHED
Object J. K. Simmons E47001 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: J. K. Simmons | Statement: [La La Land, stars, J. K. Simmons]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: J. K. Simmons
Context triple: [La La Land, stars, J. K. Simmons]
  • A. J.K. Simmons chosen
    J.K. Simmons is an American character actor known for his versatile performances in film and television, including roles in "Whiplash," the "Spider-Man" trilogy, and numerous acclaimed supporting parts.
  • B. Clarke Peters
    Clarke Peters is an American actor, writer, and director best known for his roles in acclaimed television series such as The Wire and Treme, as well as numerous film and stage performances.
  • C. Gil Bellows
    Gil Bellows is a Canadian actor best known for his roles in films like The Shawshank Redemption and the television series Ally McBeal.
  • D. Joel Murray
    Joel Murray is an American actor and comedian known for his character roles in film and television, as well as for his voice work in animated projects.
  • E. Don Cheadle
    Don Cheadle is an acclaimed American actor and filmmaker known for his versatile performances in films such as Hotel Rwanda, the Ocean’s series, and the Marvel Cinematic Universe.
  • 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_69a88713ddc88190a969715658ebe7a8 completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb81f5dac8190b5223fe2d59ee0d4 completed March 7, 2026, 5:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae2705752c81908054e8e0e426e86d completed March 9, 2026, 1:48 a.m.
Created at: March 4, 2026, 7:37 p.m.