Triple

T20651065
Position Surface form Disambiguated ID Type / Status
Subject The Phil Donahue Show E507493 entity
Predicate presenter P83 FINISHED
Object Phil Donahue 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: Phil Donahue | Statement: [The Phil Donahue Show, presenter, Phil Donahue]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Phil Donahue
Context triple: [The Phil Donahue Show, presenter, Phil Donahue]
  • A. Phil Donahue chosen
    Phil Donahue is an American television host and media pioneer best known for creating and hosting the long-running, audience-participation talk show "The Phil Donahue Show."
  • B. Don Francis
    Don Francis is an American epidemiologist and public health official known for his early work on HIV/AIDS research and prevention.
  • C. Bill Cullen
    Bill Cullen was a prolific American radio and television game show host best known for his quick wit and long-running presence on numerous classic quiz and panel programs.
  • D. Frank Cavett
    Frank Cavett was an American screenwriter best known for his work on the classic 1944 film "Going My Way."
  • E. Tom Snyder
    Tom Snyder was an American television personality and pioneering late-night talk show host known for his in-depth, conversational interview style on programs like "Tomorrow" and "The Late Late Show."
  • 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_69e0b4bf58c081908e52a4500e03ff83 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6af21e87c8190835bf2b2ef195626 completed April 20, 2026, 10:56 p.m.
Created at: April 16, 2026, 11:43 a.m.