Triple

T22538817
Position Surface form Disambiguated ID Type / Status
Subject Anne Watanabe E557228 entity
Predicate modeledFor P2006 FINISHED
Object Shiseido NE NERFINISHED

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: Shiseido | Statement: [Anne Watanabe, modeledFor, Shiseido]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Shiseido
Context triple: [Anne Watanabe, modeledFor, Shiseido]
  • A. Shiseido chosen
    Shiseido is a major Japanese multinational cosmetics and skincare company known for its high-end beauty products and long-standing global presence.
  • B. L'Oréal
    L'Oréal is a French multinational cosmetics and beauty company recognized as one of the world’s largest and most influential personal care brands.
  • C. Lancôme
    Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
  • D. Beauty Inc
    Beauty Inc is a beauty-focused media brand owned by Penske Media Corporation, covering trends, news, and analysis in the global cosmetics and personal care industry.
  • E. Biotherm
    Biotherm is a French skincare brand known for its use of aquatic ingredients and scientifically driven formulas for face and body care.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e11e58662081909ae346ab384514ca completed April 16, 2026, 5:37 p.m.
NER Named-entity recognition batch_69f15f302cd4819098c97ca4fa96363e completed April 29, 2026, 1:30 a.m.
Created at: April 16, 2026, 8:51 p.m.