Triple

T484644
Position Surface form Disambiguated ID Type / Status
Subject Loire E9847 entity
Predicate hasTributary P415 FINISHED
Object Cher E36641 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: Cher | Statement: [Loire, hasTributary, Cher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cher
Context triple: [Loire, hasTributary, Cher]
  • A. Cher chosen
    Cher is a department in central France, named after the Cher River and known for its historic towns, vineyards, and agricultural landscapes.
  • B. Bette Midler
    Bette Midler is an American singer, actress, and comedian renowned for her powerful vocals, theatrical performances, and acclaimed work in film, television, and on stage.
  • C. Dionne Warwick
    Dionne Warwick is an American singer and actress renowned for her soulful pop and R&B hits, particularly her collaborations with songwriters Burt Bacharach and Hal David.
  • D. Barbra Streisand
    Barbra Streisand is an acclaimed American singer, actress, and filmmaker known for her powerful voice, award-winning performances, and enduring influence on popular culture.
  • E. Liza Minnelli
    Liza Minnelli is an American actress and singer best known for her Academy Award-winning performance in the film "Cabaret" and her powerful stage presence in musical theatre and concerts.
  • 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_69a2e802e2908190ab17c9479e0b6412 completed Feb. 28, 2026, 1:05 p.m.
NER Named-entity recognition batch_69a2f0ba310c81909645ef7e8a20b52f completed Feb. 28, 2026, 1:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69a47d2a78a48190ac5a1e7f57f9dbd1 completed March 1, 2026, 5:53 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.