Triple

T21819842
Position Surface form Disambiguated ID Type / Status
Subject Halloween 5: The Revenge of Michael Myers E538695 entity
Predicate starring P1507 FINISHED
Object Don Shanks 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: Don Shanks | Statement: [Halloween 5: The Revenge of Michael Myers, starring, Don Shanks]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Don Shanks
Context triple: [Halloween 5: The Revenge of Michael Myers, starring, Don Shanks]
  • A. Don Shanks chosen
    Don Shanks is an American actor and stuntman best known to horror fans for playing the masked killer Michael Myers in the film "Halloween 5: The Revenge of Michael Myers."
  • B. Bill Shanks
    Bill Shanks is known primarily as the former husband of American actress Ashley Crow.
  • C. Sam Sharkey
    Sam Sharkey is a character associated with the American folklore surrounding the legendary lumberjack Paul Bunyan.
  • D. Hank O’Day
    Hank O’Day was a prominent early 20th-century Major League Baseball umpire and former pitcher, best known for his long umpiring career and involvement in several historic games and controversies.
  • E. Don Pierson
    Don Pierson was an American entrepreneur best known for founding several offshore radio stations in the 1960s that challenged broadcasting monopolies in the United Kingdom.
  • 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_69e0c475038c8190abb9b1a20eb8ff50 completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69f07cce0b8081909e20ded72db40304 completed April 28, 2026, 9:24 a.m.
Created at: April 16, 2026, 6:54 p.m.