Triple

T5694791
Position Surface form Disambiguated ID Type / Status
Subject John Tortorella E125511 entity
Predicate fullName P16 FINISHED
Object John Robert Tortorella E125511 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: John Robert Tortorella | Statement: [John Tortorella, fullName, John Robert Tortorella]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John Robert Tortorella
Context triple: [John Tortorella, fullName, John Robert Tortorella]
  • A. John Tortorella chosen
    John Tortorella is a veteran NHL coach known for his demanding, defense-first style, fiery personality, and a Stanley Cup championship with the Tampa Bay Lightning.
  • B. Mike Larocca
    Mike Larocca is a film producer known for his work on acclaimed projects including the multiverse-themed movie "Everything Everywhere All at Once."
  • C. Ray Ferraro
    Ray Ferraro is a former Canadian professional ice hockey player and prominent NHL broadcaster known for his long playing career and work as a television analyst.
  • D. Mike Piscitelli
    Mike Piscitelli is a filmmaker and photographer known for his music videos, commercials, and visual collaborations with various artists.
  • E. Jeff Maggioncalda
    Jeff Maggioncalda is a business executive best known for leading the online learning platform Coursera as its chief executive officer.
  • 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_69c0082bb19c8190823a4facd3cba79b completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c02409e70081909e47f2bd4a50fa12 completed March 22, 2026, 5:16 p.m.
NED1 Entity disambiguation (via context triple) batch_69c05a528a348190a7f6fd4cc3b76c92 completed March 22, 2026, 9:08 p.m.
Created at: March 22, 2026, 3:45 p.m.