Triple

T1408380
Position Surface form Disambiguated ID Type / Status
Subject Diane Kruger E31747 entity
Predicate modeledFor P2006 FINISHED
Object Christian Dior E58296 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: Christian Dior | Statement: [Diane Kruger, modeledFor, Christian Dior]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Christian Dior
Context triple: [Diane Kruger, modeledFor, Christian Dior]
  • A. Christian Dior chosen
    Christian Dior is a legendary French luxury fashion house renowned for its haute couture, ready-to-wear, and iconic influence on modern style.
  • B. Givenchy
    Givenchy is a renowned French luxury fashion and perfume house known for its haute couture, ready-to-wear collections, and iconic collaborations with celebrities and models.
  • C. Chanel
    Chanel is a legendary French luxury fashion house renowned for its haute couture, ready-to-wear, handbags, fragrances, and timeless designs such as the Chanel No. 5 perfume and the classic tweed suit.
  • D. Carolina Herrera
    Carolina Herrera is a Venezuelan-American fashion designer renowned for her elegant, sophisticated clothing and fragrance lines favored by celebrities and socialites.
  • E. Kenzo
    Kenzo is a Japanese masculine given name borne by various notable figures in fields such as architecture, fashion, and entertainment.
  • 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_69a49918e1f88190ba610f9dc8114578 completed March 1, 2026, 7:52 p.m.
NER Named-entity recognition batch_69a4c3bf7f0c8190aee96818de6ff4a5 completed March 1, 2026, 10:54 p.m.
NED1 Entity disambiguation (via context triple) batch_69ace578fe4c8190a4d4ced933fac0b6 completed March 8, 2026, 2:56 a.m.
Created at: March 1, 2026, 7:59 p.m.