Triple

T20427571
Position Surface form Disambiguated ID Type / Status
Subject The Girl in the Café E501044 entity
Predicate hasCastMember P2308 FINISHED
Object Elizabeth Berrington E887891 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 Berrington | Statement: [The Girl in the Café, hasCastMember, Elizabeth Berrington]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Berrington
Context triple: [The Girl in the Café, hasCastMember, Elizabeth Berrington]
  • A. Elizabeth Berrington chosen
    Elizabeth Berrington is a British actress known for her work in television, film, and theatre, including prominent roles in series such as The Syndicate.
  • B. Laura Bellingham
    Laura Bellingham is a cinematographer known for her work on the fantasy adventure film "Amulet."
  • C. Elizabeth Ayres
    Elizabeth Ayres was the wife of American Revolutionary War officer and pioneering surveyor Rufus Putnam.
  • D. Elizabeth Alington
    Elizabeth Alington was a British aristocrat and the wife of Conservative politician and former UK Prime Minister Alec Douglas-Home.
  • E. Emily Berrington
    Emily Berrington is a British actress best known for her role as the synth Niska in the television series "Humans."
  • 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_69e0b4aa68fc8190b1a14c55575ef04a completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e67ba9700481909fa23493f98095d1 completed April 20, 2026, 7:16 p.m.
NED1 Entity disambiguation (via context triple) batch_6a08d7ca41a08190bc436fb07fa3a31c completed May 16, 2026, 8:47 p.m.
Created at: April 16, 2026, 11:31 a.m.