Triple

T12917016
Position Surface form Disambiguated ID Type / Status
Subject Viktor Navorski E309010 entity
Predicate conflictsWith P4897 FINISHED
Object Frank Dixon E1008680 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: Frank Dixon | Statement: [Viktor Navorski, conflictsWith, Frank Dixon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank Dixon
Context triple: [Viktor Navorski, conflictsWith, Frank Dixon]
  • A. Frank Dixon chosen
    Frank Dixon is the strict and bureaucratic airport customs director in the film "The Terminal," who clashes with stranded traveler Viktor Navorski.
  • B. David Nobbs
    David Nobbs was a British comedy writer and novelist best known for his sharp, character-driven humor and influential work in television sitcoms and comic fiction.
  • C. Jerry Dixon
    Jerry Dixon is an American theater director, actor, and singer known for his work on and off Broadway and his long-term partnership with comedian and actor Mario Cantone.
  • D. Frank S. Black
    Frank S. Black was a Republican politician who served as the 32nd governor of New York from 1897 to 1898.
  • E. John Robie
    John Robie is a retired jewel thief known as "The Cat" who becomes embroiled in a new string of robberies on the French Riviera in Alfred Hitchcock's film "To Catch a Thief."
  • 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_69d7bdf92b588190acdf2a2291ac4590 completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d971a1e8088190af697629baecf59f completed April 10, 2026, 9:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af5df0408190a8fe83cdd91e38c9 completed May 3, 2026, 2:13 a.m.
Created at: April 9, 2026, 5:41 p.m.