Triple

T9810447
Position Surface form Disambiguated ID Type / Status
Subject Spotlight E238253 entity
Predicate stars P1956 FINISHED
Object Michael Keaton E68988 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 Keaton | Statement: [Spotlight, stars, Michael Keaton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Keaton
Context triple: [Spotlight, stars, Michael Keaton]
  • A. Michael Keaton chosen
    Michael Keaton is an American actor known for his versatile performances in films such as "Batman," "Beetlejuice," and the Academy Award–winning "Birdman."
  • B. Jonathan Irons
    Jonathan Irons is an American man whose wrongful conviction and eventual release from prison drew national attention, in part due to the advocacy and later marriage of WNBA star Maya Moore.
  • C. Adam West
    Adam West was an American actor best known for his iconic portrayal of Batman in the 1960s television series.
  • D. Burt Ward
    Burt Ward is an American actor best known for playing Robin, the Boy Wonder, in the 1960s Batman television series.
  • E. Christopher Lloyd
    Christopher Lloyd is an American actor best known for his eccentric and memorable roles in film and television, including the time-traveling scientist Doc Brown in the "Back to the Future" trilogy.
  • 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_69ca84defac48190abc1148804f184c1 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb220310c8190a16ca0b746f0ef7a completed April 2, 2026, 12:02 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc5b4dd8819088c86946b4eb8a39 completed April 5, 2026, 2:43 a.m.
Created at: March 30, 2026, 8:30 p.m.