Triple

T3965405
Position Surface form Disambiguated ID Type / Status
Subject Flesh and Stone E92204 entity
Predicate starsActor P5563 FINISHED
Object Alex Kingston E138803 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: Alex Kingston | Statement: [Flesh and Stone, starsActor, Alex Kingston]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alex Kingston
Context triple: [Flesh and Stone, starsActor, Alex Kingston]
  • A. Alex Kingston chosen
    Alex Kingston is an English actress best known for her roles in the television series "ER" and "Doctor Who," as well as numerous film and stage productions.
  • B. Erica Durance
    Erica Durance is a Canadian actress best known for playing Lois Lane on the television series "Smallville."
  • C. Lauren Cohan
    Lauren Cohan is an American-British actress best known for playing Maggie Greene on the television series "The Walking Dead."
  • D. Nathalie Emmanuel
    Nathalie Emmanuel is a British actress best known for her roles as Missandei in "Game of Thrones" and Ramsey in the "Fast & Furious" film franchise.
  • E. Alafair Burke
    Alafair Burke is an American crime novelist, law professor, and former prosecutor known for her contemporary suspense novels and collaborations on bestselling mystery series.
  • 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_69aed96624188190ac8c45bb57ab72b5 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aef97520588190922e56201fc3ca52 completed March 9, 2026, 4:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c412f6481908e2da2e3de365f20 completed March 14, 2026, 11:53 a.m.
Created at: March 9, 2026, 3:31 p.m.