Triple
T19788237
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sadie Liza Vaughan |
E475333
|
entity |
| Predicate | fashionLabel |
P48228
|
FINISHED |
| Object | Frost French |
—
|
NE NERFINISHED |
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: Frost French | Statement: [Sadie Liza Vaughan, fashionLabel, Frost French]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frost French Context triple: [Sadie Liza Vaughan, fashionLabel, Frost French]
-
A.
Frost French
chosen
Frost French is a British fashion label known for its quirky, vintage-inspired womenswear and lingerie, co-founded by actress and designer Sadie Frost.
-
B.
Louis (French)
Louis is the French given name corresponding to the name Ludwik in other languages.
-
C.
The French
The French is a renowned fine-dining restaurant in Manchester’s Midland Hotel, known for its modern British cuisine and historic, elegant setting.
-
D.
Jean (French)
Jean is the standard French given name equivalent to the English name John, widely used for men in French-speaking countries.
-
E.
Franzese
Franzese is an Italian surname borne by various notable individuals in fields such as entertainment and organized crime.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e51b014081908b263e167370529a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65389c9ac81909e61b3cbb9213e72 |
completed | April 20, 2026, 4:25 p.m. |
Created at: April 10, 2026, 1:49 p.m.