Triple

T10861171
Position Surface form Disambiguated ID Type / Status
Subject Joan Caulfield E256406 entity
Predicate spouse P13 FINISHED
Object Frank Ross E502675 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: Frank Ross | Statement: [Joan Caulfield, spouse, Frank Ross]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frank Ross
Context triple: [Joan Caulfield, spouse, Frank Ross]
  • A. Frank Ross chosen
    Frank Ross was an American film producer known for his work on major mid-20th-century Hollywood productions.
  • B. William Ross
    William Ross is an American composer, orchestrator, and conductor known for his work on numerous film scores and collaborations with major Hollywood productions.
  • C. Charles Wood
    Charles Wood was an Irish-born composer and influential teacher associated with the English Musical Renaissance, best known for his Anglican church music and role in shaping early 20th-century British composers.
  • D. Charles Wood
    Charles Wood was a British playwright and screenwriter known for his sharp, satirical writing and influential work in film, television, and theatre.
  • E. John Marsh
    John Marsh was the husband of American novelist Margaret Mitchell, best known for supporting her during the creation of "Gone with the Wind."
  • 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_69d6aa83d1448190a66d93c32394d21f completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d7515186f08190a5cc388a7d936c4f completed April 9, 2026, 7:12 a.m.
NED1 Entity disambiguation (via context triple) batch_69e3e6d27d8c8190b2c8ee9c54cf7fd1 completed April 18, 2026, 8:17 p.m.
Created at: April 8, 2026, 9:20 p.m.