Triple

T2369190
Position Surface form Disambiguated ID Type / Status
Subject Fran Walsh E46049 entity
Predicate givenName P17 FINISHED
Object Frances E12143 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: Frances | Statement: [Fran Walsh, givenName, Frances]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Frances
Context triple: [Fran Walsh, givenName, Frances]
  • A. Frances chosen
    Frances is a feminine given name of Latin origin, commonly used in English-speaking countries.
  • B. Mariana
    "Mariana" is a famous 1851 Pre-Raphaelite painting by John Everett Millais depicting a solitary woman in a richly detailed interior, inspired by Shakespeare’s "Measure for Measure" and Tennyson’s poem of the same name.
  • C. Clare
    Clare is a central character in the Restoration comedy "The Witty Fair One," known for embodying the play’s themes of wit, romance, and social intrigue.
  • D. Niles
    Niles is a historic former town in California, now a district of Fremont, known for its early silent film industry and railroad heritage.
  • E. Collier
    Collier is a surname most prominently associated in sports with Napheesa Collier, an American professional basketball player and WNBA All-Star.
  • 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_69a88a145268819083e2736cb835c696 completed March 4, 2026, 7:37 p.m.
NER Named-entity recognition batch_69abc76dcaa481908567a068bd61e5ad completed March 7, 2026, 6:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69aea89eb46481909cc01202839d417f completed March 9, 2026, 11:01 a.m.
Created at: March 4, 2026, 7:56 p.m.