Triple

T3884075
Position Surface form Disambiguated ID Type / Status
Subject Bus Stop E92895 entity
Predicate hasNominee P16414 FINISHED
Object Don Murray E408985 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: Don Murray | Statement: [Bus Stop, hasNominee, Don Murray]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Don Murray
Context triple: [Bus Stop, hasNominee, Don Murray]
  • A. Don Murray chosen
    Don Murray is an American actor best known for his Oscar-nominated film debut in the 1956 drama "Bus Stop" opposite Marilyn Monroe.
  • B. Ken Morrow
    Ken Morrow is an American former defenseman best known for winning gold with the "Miracle on Ice" 1980 U.S. Olympic hockey team and then capturing four consecutive Stanley Cups with the New York Islanders.
  • C. Luke Murray
    Luke Murray is an American basketball coach known for his assistant coaching roles at several major college programs and as the son of actor Bill Murray.
  • D. Mel Hunter
    Mel Hunter was an American illustrator and artist best known for his science fiction book and magazine covers in the mid-20th century.
  • E. Ray McKinnon
    Ray McKinnon is an American actor, writer, producer, and director known for his character roles in film and television and for creating the acclaimed series "Rectify."
  • 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_69aed9697de0819087c2559295ff3d12 completed March 9, 2026, 2:30 p.m.
NER Named-entity recognition batch_69aeec9029908190a7b36a3827734db1 completed March 9, 2026, 3:51 p.m.
NED1 Entity disambiguation (via context triple) batch_69b562821c3c81909805cb877288405b completed March 14, 2026, 1:28 p.m.
Created at: March 9, 2026, 3:20 p.m.