Triple
T3233331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kendall Jenner |
E67792
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object | 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: Dior | Statement: [Kendall Jenner, hasModeledFor, Dior]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dior Context triple: [Kendall Jenner, hasModeledFor, Dior]
-
A.
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.
-
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.
Nina Ricci
Nina Ricci is a French luxury fashion house renowned for its elegant haute couture, ready-to-wear collections, and iconic fragrances.
-
D.
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.
-
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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaedb718c8190aae12f763033713a |
completed | March 8, 2026, 5:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e82b6bfc8190a6db566c37ef2eff |
completed | March 12, 2026, 4:22 p.m. |
Created at: March 8, 2026, 3:08 p.m.