Triple

T14162800
Position Surface form Disambiguated ID Type / Status
Subject The Courier E350990 entity
Predicate stars P1956 FINISHED
Object Jessie Buckley E963872 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: Jessie Buckley | Statement: [The Courier, stars, Jessie Buckley]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Jessie Buckley
Context triple: [The Courier, stars, Jessie Buckley]
  • A. Jessie Buckley chosen
    Jessie Buckley is an Irish actress and singer known for her acclaimed performances in film, television, and theatre, including roles in works like "Wild Rose," "Chernobyl," and "The Lost Daughter."
  • B. Daisy Edgar-Jones
    Daisy Edgar-Jones is a British actress best known for her breakout role in the television adaptation of Sally Rooney’s "Normal People."
  • C. Lucy Boynton
    Lucy Boynton is a British-American actress known for her roles in films such as "Bohemian Rhapsody" and "Sing Street," as well as various television dramas.
  • D. Florence Pugh
    Florence Pugh is an English actress acclaimed for her emotionally intense and versatile performances in films such as "Lady Macbeth," "Midsommar," and "Little Women."
  • E. Danielle Macdonald
    Danielle Macdonald is an Australian actress known for her breakout roles in films such as "Patti Cake$," "Dumplin'," and the drama "Skin."
  • 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_69d8278775fc8190b0802d22ca2f495d completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de613a4a2081908fd51bf4b4d82b6c completed April 14, 2026, 3:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69fcf7f3170481909f3981c1e56235d9 completed May 7, 2026, 8:37 p.m.
Created at: April 10, 2026, 12:59 a.m.