Triple

T15546812
Position Surface form Disambiguated ID Type / Status
Subject James Sikking E370629 entity
Predicate name P16 FINISHED
Object James Sikking 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: James Sikking | Statement: [James Sikking, name, James Sikking]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: James Sikking
Context triple: [James Sikking, name, James Sikking]
  • A. James Sikking chosen
    James Sikking is an American actor best known for his role as the strict and disciplined Lt. Howard Hunter on the acclaimed television series "Hill Street Blues."
  • B. Chris Sievernich
    Chris Sievernich is a German film producer best known for his work on acclaimed art-house and independent films, including Wim Wenders’ "Paris, Texas."
  • C. Ian Gelder
    Ian Gelder is a British actor best known for his work in television, film, and theatre, including roles in high-profile series such as Game of Thrones.
  • D. Brian Sigler
    Brian Sigler is the brother of American actress and singer Jamie-Lynn Sigler, known for her role on the television series "The Sopranos."
  • E. Jerry Seeman
    Jerry Seeman was a prominent NFL official who served as a referee in multiple Super Bowls and later became the league’s Director of Officiating.
  • 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_69d85cc521a08190921fb50319dddc34 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a9073948190b6e9cf504aacc7cf completed April 16, 2026, 2:33 a.m.
Created at: April 10, 2026, 4:08 a.m.