Triple

T2486937
Position Surface form Disambiguated ID Type / Status
Subject Marriage Story E55947 entity
Predicate character P662 FINISHED
Object Nora Fanshaw E203660 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: Nora Fanshaw | Statement: [Marriage Story, character, Nora Fanshaw]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nora Fanshaw
Context triple: [Marriage Story, character, Nora Fanshaw]
  • A. Nora Fanshaw chosen
    Nora Fanshaw is a sharp, high-powered divorce lawyer character in the film "Marriage Story," portrayed by Laura Dern.
  • B. Lucy Aikin
    Lucy Aikin was a prominent English historian, biographer, and writer of the late 18th and early 19th centuries, best known for her historical works on the courts of Elizabeth I and James I.
  • C. Fanny Eden
    Fanny Eden was a member of the British Eden family in colonial India, after whom the famous cricket ground Eden Gardens in Kolkata was named.
  • D. Ambrosine Phillpotts
    Ambrosine Phillpotts was a British character actress known for her numerous supporting roles in mid-20th-century film, theatre, and television.
  • E. Eleanor Witcombe
    Eleanor Witcombe was an Australian screenwriter and playwright known for her influential adaptations and contributions to Australian film, television, and radio drama.
  • 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_69ab49e670a88190b928e08302381710 completed March 6, 2026, 9:40 p.m.
NER Named-entity recognition batch_69abd1782ca081909645164a6acf0ea0 completed March 7, 2026, 7:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69af17bca6f88190a63672fb3372f0be completed March 9, 2026, 6:55 p.m.
Created at: March 6, 2026, 9:45 p.m.