Triple

T2013397
Position Surface form Disambiguated ID Type / Status
Subject Académie Julian E43739 entity
Predicate student P7251 FINISHED
Object Elizabeth Nourse E354571 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: Elizabeth Nourse | Statement: [Académie Julian, student, Elizabeth Nourse]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Elizabeth Nourse
Context triple: [Académie Julian, student, Elizabeth Nourse]
  • A. Elizabeth Nourse chosen
    Elizabeth Nourse was an American realist painter known for her sensitive depictions of women and rural life, who built a successful career in Paris in the late 19th and early 20th centuries.
  • B. Elizabeth Reaser
    Elizabeth Reaser is an American actress best known for her roles in the Twilight film series and the television drama Grey's Anatomy.
  • C. Jane Belson
    Jane Belson was a British barrister best known as the wife of author Douglas Adams.
  • D. Marjorie Reynolds
    Marjorie Reynolds was an American film and television actress best known for her roles in classic 1940s movies and early TV series.
  • E. Helen Hughes
    Helen Hughes was a daughter of Charles Evans Hughes, the prominent American statesman who served as both U.S. Secretary of State and Chief Justice of the Supreme Court.
  • 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_69a88716e9f08190946313fdc949e3cf completed March 4, 2026, 7:25 p.m.
NER Named-entity recognition batch_69abb8b42d508190bf2b63132bb2ad77 completed March 7, 2026, 5:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69b5d0232d78819084fbc7c1d229c83c completed March 14, 2026, 9:16 p.m.
Created at: March 4, 2026, 7:37 p.m.