Triple
T7909680
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Agyness Deyn |
E183665
|
entity |
| Predicate | modeledFor |
P2006
|
FINISHED |
| Object | Burberry |
E317091
|
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: Burberry | Statement: [Agyness Deyn, modeledFor, Burberry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Burberry Context triple: [Agyness Deyn, modeledFor, Burberry]
-
A.
Burberry
chosen
Burberry is a British luxury fashion house renowned for its iconic trench coats, distinctive check pattern, and heritage-inspired apparel and accessories.
-
B.
Loewe
Loewe is a Spanish luxury fashion house renowned for its high-end leather goods, ready-to-wear, and accessories.
-
C.
Balmain
Balmain is a historic inner-west suburb of Sydney, Australia, known for its waterfront location on Sydney Harbour, preserved Victorian architecture, and vibrant pub and café culture.
-
D.
Balmain
Balmain is a French luxury fashion house renowned for its opulent, sharply tailored designs and influential presence on international runways.
-
E.
Hugo Boss
Hugo Boss is a German luxury fashion house known for its high-end menswear, fragrances, and accessories.
- 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a5db9508190bbe92673ef5a7861 |
completed | March 31, 2026, 3:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cb5bd54f548190916bf00852f37224 |
completed | March 31, 2026, 5:29 a.m. |
Created at: March 30, 2026, 5:04 p.m.