Triple

T13383477
Position Surface form Disambiguated ID Type / Status
Subject Sally Hay Burton E319375 entity
Predicate hasRelativeByMarriage P7844 FINISHED
Object Kate Burton E319376 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: Kate Burton | Statement: [Sally Hay Burton, hasRelativeByMarriage, Kate Burton]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kate Burton
Context triple: [Sally Hay Burton, hasRelativeByMarriage, Kate Burton]
  • A. Kate Burton chosen
    Kate Burton is a British-American actress known for her work on stage and in television series such as "Grey's Anatomy" and "Scandal."
  • B. Lara Pulver
    Lara Pulver is a British actress known for her roles in television series such as "Sherlock" and "Spooks," as well as various film and stage productions.
  • C. Helen Gibson
    Helen Gibson was a pioneering American silent film actress and stunt performer, best known as one of early cinema’s first professional stuntwomen.
  • D. Martha Fiennes
    Martha Fiennes is a British film director, writer, and producer best known for her visually distinctive adaptation of "Onegin."
  • E. Anneke Wills
    Anneke Wills is a British actress best known for playing the companion Polly in the classic science fiction television series Doctor Who during the 1960s.
  • 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_69d806b886bc8190b676e7768b8e01c5 completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69dadce80158819082156eaeaeda3bd8 completed April 11, 2026, 11:44 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7268cf04c8190a35fd48ce81c149e completed May 3, 2026, 10:42 a.m.
Created at: April 9, 2026, 9:33 p.m.