Triple

T17965736
Position Surface form Disambiguated ID Type / Status
Subject Joe Viterelli E449199 entity
Predicate notableWork P4 FINISHED
Object Mickey Blue Eyes NE NERFINISHED

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: Mickey Blue Eyes | Statement: [Joe Viterelli, notableWork, Mickey Blue Eyes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mickey Blue Eyes
Context triple: [Joe Viterelli, notableWork, Mickey Blue Eyes]
  • A. Mickey Blue Eyes chosen
    Mickey Blue Eyes is a 1999 romantic comedy film starring Hugh Grant as an English auctioneer who becomes entangled with the New York Mafia through his fiancée’s family.
  • B. Mickey One
    Mickey One is a 1965 avant-garde crime drama film starring Warren Beatty as a paranoid stand-up comic on the run from the mob.
  • C. Mickey
    Mickey is the central protagonist of the 1938 horse-racing drama film "Stablemates," around whom the story’s emotional and narrative arc revolves.
  • D. Mickey
    Mickey is the nickname of Mickey Rivers, a former Major League Baseball center fielder known for his speed and leadoff hitting, especially with the New York Yankees in the late 1970s.
  • E. Mickey
    Mickey is the commonly used nickname of Mickey Leland, an American congressman and humanitarian known for his work on hunger and poverty issues.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8b9f9927c8190a006110c8b996e61 completed April 10, 2026, 8:51 a.m.
NER Named-entity recognition batch_69e4b1380960819089a3c0dd7cd57e5e completed April 19, 2026, 10:40 a.m.
Created at: April 10, 2026, 10:22 a.m.