Triple

T3514867
Position Surface form Disambiguated ID Type / Status
Subject Woman of the Year E74281 entity
Predicate storyBy P1955 FINISHED
Object Michael Kanin E365507 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: Michael Kanin | Statement: [Woman of the Year, storyBy, Michael Kanin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Michael Kanin
Context triple: [Woman of the Year, storyBy, Michael Kanin]
  • A. Michael Kanin chosen
    Michael Kanin was an American screenwriter best known for his Academy Award–winning work in classic Hollywood films, often in collaboration with his wife, Fay Kanin.
  • B. Joseph Kane
    Joseph Kane was an American film director best known for his prolific work on B-Western movies during the mid-20th century.
  • C. Benjamin Kanes
    Benjamin Kanes is an American actor and filmmaker known for supporting roles in film and television, including appearances in projects like "The Visit."
  • D. Murray Wier
    Murray Wier was an American professional basketball player best known for his collegiate stardom at the University of Iowa in the 1940s and subsequent career in the early NBA.
  • E. Kurt Kasznar
    Kurt Kasznar was an Austrian-American character actor known for his prolific work in mid-20th-century film, television, and theater, often portraying urbane or authoritative supporting roles.
  • 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_69ad85cfb5c881909c9a2edd9d6043cc completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adbc2efa4c8190ac1ca221f3f0eba4 completed March 8, 2026, 6:13 p.m.
NED1 Entity disambiguation (via context triple) batch_69b38bc4e9988190921d193e84e53bf1 completed March 13, 2026, 4 a.m.
Created at: March 8, 2026, 3:19 p.m.