Triple

T8963296
Position Surface form Disambiguated ID Type / Status
Subject Don Rickles E214061 entity
Predicate influencedBy P9 FINISHED
Object Jack Benny E61535 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 Benny | Statement: [Don Rickles, influencedBy, Jack Benny]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jack Benny
Context triple: [Don Rickles, influencedBy, Jack Benny]
  • A. Jack Benny chosen
    Jack Benny was a pioneering American comedian, actor, and radio/television star renowned for his masterful timing, stingy persona, and influential deadpan style.
  • B. Fred Allen
    Fred Allen was an American film editor active in mid-20th-century cinema, known for his work on numerous Hollywood productions.
  • C. Fred Allen
    Fred Allen was a prominent American comedian and radio host best known for his witty, satirical radio programs during the 1930s and 1940s.
  • D. Red Skelton
    Red Skelton was a beloved American comedian, actor, and radio and television entertainer best known for his long-running TV variety show and iconic clown characters.
  • E. Milton Berle
    Milton Berle was an American comedian and actor widely known as one of television’s first major stars, often called “Mr. Television” for his pioneering role in early TV entertainment.
  • 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_69ca839cd6008190a1546a701a56710c completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cc674b06f08190b2d992674a093592 completed April 1, 2026, 12:31 a.m.
NED1 Entity disambiguation (via context triple) batch_69cfdb8aff9c81908554788f583925bf completed April 3, 2026, 3:23 p.m.
Created at: March 30, 2026, 7:01 p.m.