Triple

T9824640
Position Surface form Disambiguated ID Type / Status
Subject Lindsay Doran E238622 entity
Predicate hasWorkedWith P9615 FINISHED
Object Emma Thompson E3055 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: Emma Thompson | Statement: [Lindsay Doran, hasWorkedWith, Emma Thompson]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Emma Thompson
Context triple: [Lindsay Doran, hasWorkedWith, Emma Thompson]
  • A. Emma Thompson chosen
    Emma Thompson is an acclaimed British actress and screenwriter known for her Oscar-winning performances and adaptations in films such as "Howards End" and "Sense and Sensibility."
  • B. Joanna Lumley
    Joanna Lumley is a British actress, presenter, and former model best known for her iconic role as Patsy Stone in the television series "Absolutely Fabulous."
  • C. Dawn French
    Dawn French is a British comedian and actress best known as one half of the comedy duo French and Saunders and for her acclaimed television work in shows such as "The Vicar of Dibley."
  • D. Cheryl Hines
    Cheryl Hines is an American actress and director best known for her role as Larry David’s wife, Cheryl, on the HBO series "Curb Your Enthusiasm."
  • 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 (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_69ca84e0dd1881909800765d1e21f735 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cdb3181c688190afea3b27ee392a30 completed April 2, 2026, 12:06 a.m.
NED1 Entity disambiguation (via context triple) batch_69d1cc810bac8190a5ff94c0717e7706 completed April 5, 2026, 2:44 a.m.
Created at: March 30, 2026, 8:31 p.m.