Triple
T8690323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miranda Kerr |
E206269
|
entity |
| Predicate | modeledFor |
P2006
|
FINISHED |
| Object | Balenciaga |
E58295
|
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: Balenciaga | Statement: [Miranda Kerr, modeledFor, Balenciaga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Balenciaga Context triple: [Miranda Kerr, modeledFor, Balenciaga]
-
A.
Balenciaga
chosen
Balenciaga is a luxury French fashion house renowned for its avant-garde, architectural designs and influential role in high fashion.
-
B.
Balmain
Balmain is a French luxury fashion house renowned for its opulent, sharply tailored designs and influential presence on international runways.
-
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.
Prada
Prada is a renowned Italian luxury fashion house known for its high-end clothing, leather goods, and accessories.
-
E.
Gucci
Gucci is a renowned Italian luxury fashion house known for its high-end clothing, accessories, and iconic branding.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5734602c81909a0687e00f4a4a26 |
completed | March 31, 2026, 11:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cef3df73b88190b67138ee5129de8b |
completed | April 2, 2026, 10:55 p.m. |
Created at: March 30, 2026, 6:33 p.m.