Triple

T10737147
Position Surface form Disambiguated ID Type / Status
Subject Tamsin E253222 entity
Predicate associatedWith P37 FINISHED
Object Tamsin Egerton E51654 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: Tamsin Egerton | Statement: [Tamsin, associatedWith, Tamsin Egerton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tamsin Egerton
Context triple: [Tamsin, associatedWith, Tamsin Egerton]
  • A. Tamsin Egerton chosen
    Tamsin Egerton is an English actress and model known for roles in films such as "St Trinian's," "Keeping Mum," and "The Look of Love."
  • B. Sophia Di Martino
    Sophia Di Martino is a British actress best known for her role as Sylvie in the Marvel series "Loki" and for appearances in films and television including the romantic comedy "Yesterday."
  • C. Lily James
    Lily James is an English actress known for her roles in films such as Cinderella, Baby Driver, and Mamma Mia! Here We Go Again, as well as the TV series Downton Abbey.
  • D. Katherine Hoult
    Katherine Hoult is known as the spouse of Richard Mather.
  • E. Lily Collins
    Lily Collins is a British-American actress and model known for roles in films like "Mirror Mirror" and the series "Emily in Paris."
  • 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_69d6aa5e51e8819095f06881cecf152e completed April 8, 2026, 7:19 p.m.
NER Named-entity recognition batch_69d710410a04819090036597ac0d271c completed April 9, 2026, 2:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69de22dce1cc8190a3511d86e8bd6d3e completed April 14, 2026, 11:19 a.m.
Created at: April 8, 2026, 9:14 p.m.