Triple

T7436836
Position Surface form Disambiguated ID Type / Status
Subject Cold Mountain E171636 entity
Predicate starring P1507 FINISHED
Object Kathy Baker E221087 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: Kathy Baker | Statement: [Cold Mountain, starring, Kathy Baker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kathy Baker
Context triple: [Cold Mountain, starring, Kathy Baker]
  • A. Kathy Baker chosen
    Kathy Baker is an American actress acclaimed for her nuanced performances in film and television, particularly for her role in the series "Picket Fences."
  • B. Kathryn Blair
    Kathryn Blair is the daughter of former UK Prime Minister Tony Blair and prominent barrister Cherie Booth.
  • C. Virginia Weidler
    Virginia Weidler was an American child actress of the 1930s and 1940s, best remembered for her witty supporting roles in classic Hollywood films such as "The Philadelphia Story."
  • D. Kathy Speer
    Kathy Speer is an American television writer and producer best known for her work on popular sitcoms such as The Golden Girls and its spin-off The Golden Palace.
  • E. Barbara Johnson
    Barbara Johnson was an influential American literary critic and translator known for her pioneering work in deconstruction, feminist theory, and the analysis of race and gender in literature.
  • 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_69c68a64228c8190affaec2a8127ce7b completed March 27, 2026, 1:47 p.m.
NER Named-entity recognition batch_69c6f349399c8190b46d5882ece2e73a completed March 27, 2026, 9:14 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1c3ed827c8190899cb2ae9561765e completed April 5, 2026, 2:07 a.m.
Created at: March 27, 2026, 3:13 p.m.