Triple

T16909677
Position Surface form Disambiguated ID Type / Status
Subject Dan Vasser E410161 entity
Predicate relative P37 FINISHED
Object Jack Vasser E1241156 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: Jack Vasser | Statement: [Dan Vasser, relative, Jack Vasser]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jack Vasser
Context triple: [Dan Vasser, relative, Jack Vasser]
  • A. Jack Vasser chosen
    Jack Vasser is a character in the television series "Journeyman," known primarily as the brother of the time-traveling protagonist Dan Vasser.
  • B. Dan Vasser
    Dan Vasser is the time-traveling newspaper reporter protagonist of the television series "Journeyman," whose sudden jumps through time complicate his personal and professional life.
  • C. Jon DeVaan
    Jon DeVaan is a longtime Microsoft engineering leader known for his key roles in developing and managing core Windows and Office technologies.
  • D. David Wickes
    David Wickes is a British film and television director known for his work on crime dramas and period pieces, including notable adaptations of classic detective stories.
  • E. George Ranft
    George Ranft, better known as George Raft, was an American film actor and dancer famed for his portrayals of suave gangsters in 1930s and 1940s Hollywood crime dramas.
  • 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_69d886c7b1e481908c3766dfa8c13458 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3ca3bdc3081908a9b4f6e63405348 completed April 18, 2026, 6:15 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00d458902481908f79cd5a9f72f7fd completed May 10, 2026, 6:54 p.m.
Created at: April 10, 2026, 5:30 a.m.