Triple

T14645986
Position Surface form Disambiguated ID Type / Status
Subject Edgar Bergen E343849 entity
Predicate child P120 FINISHED
Object Candice Bergen E76795 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: Candice Bergen | Statement: [Edgar Bergen, child, Candice Bergen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Candice Bergen
Context triple: [Edgar Bergen, child, Candice Bergen]
  • A. Candice Bergen chosen
    Candice Bergen is an American actress and former fashion model best known for her Emmy-winning role as the sharp-tongued journalist Murphy Brown on the hit television sitcom of the same name.
  • B. Karen Kline
    Karen Kline is an American psychotherapist best known as the longtime spouse of Academy Award–winning actress Linda Hunt.
  • C. Alley Mills
    Alley Mills is an American actress best known for her role as Norma Arnold, the mother on the classic television series "The Wonder Years."
  • D. Danielle Kaye
    Danielle Kaye is known as the spouse of British film director and music video creator Tony Kaye.
  • E. Nancy Allen
    Nancy Allen is an American actress best known for her roles in films such as "Carrie," "Dressed to Kill," and the "RoboCop" series.
  • 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_69d822e1a2cc81908e5bb93cf61ce3cc completed April 9, 2026, 10:06 p.m.
NER Named-entity recognition batch_69deb4ea6d8481908e6331ca173c646b completed April 14, 2026, 9:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69fee5dc9b908190b1d7583810dc9c41 completed May 9, 2026, 7:44 a.m.
Created at: April 10, 2026, 1:26 a.m.