Triple

T13022970
Position Surface form Disambiguated ID Type / Status
Subject Lori Nichol E326222 entity
Predicate hasClient P734 FINISHED
Object Yuka Sato E760561 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: Yuka Sato | Statement: [Lori Nichol, hasClient, Yuka Sato]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Yuka Sato
Context triple: [Lori Nichol, hasClient, Yuka Sato]
  • A. Yuka Sato chosen
    Yuka Sato is a Japanese figure skater and 1994 World Champion known for her elegant style and successful professional career.
  • B. Yuki Satō
    Yuki Satō is a Japanese name shared by multiple notable individuals, including athletes and entertainers, distinguished in their respective fields.
  • C. Yui Satō
    Yui Satō is a Japanese given name borne by multiple notable individuals, including figures in entertainment and other public fields.
  • D. Takako Suzuki
    Takako Suzuki is a Japanese politician and member of the House of Representatives, known for her work in regional revitalization and as the daughter of former Prime Minister Zenko Suzuki.
  • E. Yuki Nagasato
    Yuki Nagasato is a Japanese professional footballer and World Cup–winning forward renowned for her prolific international career and success in top women’s leagues in Japan, Germany, and the United States.
  • 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_69d8076cc45c81908123123f43e69266 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69d97ed05e9c8190a4f208662bca0602 completed April 10, 2026, 10:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69f71f0dddc88190918d3a071b75a556 completed May 3, 2026, 10:10 a.m.
Created at: April 9, 2026, 8:52 p.m.