Triple

T3854930
Position Surface form Disambiguated ID Type / Status
Subject Lady in the Water E89989 entity
Predicate editedBy P1954 FINISHED
Object Barbara Tulliver E134609 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: Barbara Tulliver | Statement: [Lady in the Water, editedBy, Barbara Tulliver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barbara Tulliver
Context triple: [Lady in the Water, editedBy, Barbara Tulliver]
  • A. Barbara Tulliver chosen
    Barbara Tulliver is a film editor best known for her long-standing collaboration with director Paul Thomas Anderson on several of his acclaimed movies.
  • B. Mary Tuffley
    Mary Tuffley was the wife of English writer Daniel Defoe, known primarily through her marriage to the famed author of "Robinson Crusoe."
  • C. Ambrosine Phillpotts
    Ambrosine Phillpotts was a British character actress known for her numerous supporting roles in mid-20th-century film, theatre, and television.
  • D. Edith Murgatroyd
    Edith Murgatroyd was a British actress active during the silent film era, known for her role in early 1920s cinema.
  • E. Elizabeth Bottomley
    Elizabeth Bottomley was the wife of Robert N. Noyce, the pioneering co-founder of Intel and a key figure in the development of the integrated circuit.
  • 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_69aed95b3c088190a8f85d19e6070599 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aeec05ec4c8190bd5e5463163712dc completed March 9, 2026, 3:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5041f40f88190883339db0950026a completed March 14, 2026, 6:45 a.m.
Created at: March 9, 2026, 3:19 p.m.