Triple
T3428400
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | LVMH |
E72279
|
entity |
| Predicate | ownsBrand |
P1500
|
FINISHED |
| Object | Kenzo |
E126072
|
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: Kenzo | Statement: [LVMH, ownsBrand, Kenzo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kenzo Context triple: [LVMH, ownsBrand, Kenzo]
-
A.
Kenzo
chosen
Kenzo is a Japanese masculine given name borne by various notable figures in fields such as architecture, fashion, and entertainment.
-
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.
Herve
Herve is a municipality in the province of Liège in Wallonia, eastern Belgium, known for its rural landscape and traditional Herve cheese.
-
D.
Armani
Armani is a renowned Italian luxury fashion house celebrated worldwide for its elegant, minimalist designs in clothing, accessories, and fragrances.
-
E.
Nina Ricci
Nina Ricci is a French luxury fashion house renowned for its elegant haute couture, ready-to-wear collections, and iconic fragrances.
- 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_69ad85ae14308190bcbc25cfa0246c0b |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb983f4608190abcc27aa7b926deb |
completed | March 8, 2026, 6:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b35478448481908e1c0f717d99f992 |
completed | March 13, 2026, 12:04 a.m. |
Created at: March 8, 2026, 3:15 p.m.