Triple

T20610045
Position Surface form Disambiguated ID Type / Status
Subject Silk E506423 entity
Predicate portrayedBy P1507 FINISHED
Object Frances Barber 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: Frances Barber | Statement: [Silk, portrayedBy, Frances Barber]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frances Barber
Context triple: [Silk, portrayedBy, Frances Barber]
  • A. Frances Barber chosen
    Frances Barber is an English actress known for her extensive work in film, television, and theatre, including roles in productions such as "Film Stars Don’t Die in Liverpool."
  • B. Wendy Hiller
    Wendy Hiller was an acclaimed English stage and film actress known for her nuanced, often understated performances in classics such as "Pygmalion" and "Separate Tables."
  • C. Lesley Garrett
    Lesley Garrett is an English soprano and media personality known for her operatic performances and popular classical crossover work.
  • D. Ann Pugh
    Ann Pugh is known as the spouse of British Liberal Democrat politician John David Pugh, who served as Member of Parliament for Southport.
  • E. Betsy Aidem
    Betsy Aidem is an American actress known for her work in film, television, and theater.
  • 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_69e0b4bb2b4081908fa4a72444120f35 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6aad6f53481908fb242947dda7028 completed April 20, 2026, 10:38 p.m.
Created at: April 16, 2026, 11:41 a.m.