Triple

T21041392
Position Surface form Disambiguated ID Type / Status
Subject Beggar Woman E518333 entity
Predicate relative P37 FINISHED
Object Johanna Barker NE NERFINISHED

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: Johanna Barker | Statement: [Beggar Woman, relative, Johanna Barker]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Johanna Barker
Context triple: [Beggar Woman, relative, Johanna Barker]
  • A. Johanna Barker chosen
    Johanna Barker is the innocent and sheltered daughter of Sweeney Todd in the 2007 film adaptation of the musical thriller, serving as a central figure in the story’s themes of lost family and doomed romance.
  • B. Maria Bicknell
    Maria Bicknell was the wife of English Romantic landscape painter John Constable and a member of a well-connected Suffolk family in early 19th-century England.
  • C. Jean Barker
    Jean Barker is a journalist best known for serving as editor of the publication *The Beachcomber*.
  • D. Frances Barker
    Frances Barker was the wife of William Shirley, the 18th-century British colonial governor of Massachusetts.
  • E. Mary Barstow
    Mary Barstow was the second wife of American illustrator Norman Rockwell and the mother of his three sons.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b50438e08190917e2538bb8bc034 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e6fcefe4688190ad1bed1ef2d7a3e5 completed April 21, 2026, 4:28 a.m.
Created at: April 16, 2026, 2:15 p.m.