Triple

T15474920
Position Surface form Disambiguated ID Type / Status
Subject Stay (2005 film) E376758 entity
Predicate castMember P1668 FINISHED
Object Elizabeth Reaser E224211 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: Elizabeth Reaser | Statement: [Stay (2005 film), castMember, Elizabeth Reaser]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Reaser
Context triple: [Stay (2005 film), castMember, Elizabeth Reaser]
  • A. Elizabeth Reaser chosen
    Elizabeth Reaser is an American actress best known for her roles in the Twilight film series and the television drama Grey's Anatomy.
  • B. Elizabeth Anweis
    Elizabeth Anweis is an American actress known for her work in film and television, including roles in projects such as The Caine Mutiny Court-Martial (2023).
  • C. Margaret Boals
    Margaret Boals is the daughter of acclaimed American character actress Margo Martindale.
  • D. Betsy Blair
    Betsy Blair was an American actress best known for her acclaimed, Oscar-nominated performance in the 1955 film "Marty" and for her work in both Hollywood and European cinema.
  • E. Elizabeth Nourse
    Elizabeth Nourse was an American realist painter known for her sensitive depictions of women and rural life, who built a successful career in Paris in the late 19th and early 20th centuries.
  • 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_69d85cd21dcc81908646251b1c26ea00 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f6e859481909c3d08343b7ad27c completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a07fdaed2c88190b96f7de279fffa39 completed May 16, 2026, 5:16 a.m.
Created at: April 10, 2026, 3:34 a.m.