Triple
T30255868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Margaret Whigham |
E769339
|
entity |
| Predicate | fashionSense |
P42256
|
FINISHED |
| Object | noted for elegance and style |
—
|
LITERAL 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: noted for elegance and style | Statement: [Margaret Whigham, fashionSense, noted for elegance and style]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fashionSense Context triple: [Margaret Whigham, fashionSense, noted for elegance and style]
-
A.
fashionStyle
chosen
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
B.
fashionCategory
Indicates the classification of an item into a specific fashion-related category or type (e.g., clothing, footwear, accessories).
-
C.
fashionLabel
Indicates that an entity is a fashion brand or label associated with the design, production, or marketing of clothing or accessories.
-
D.
fashionItem
Indicates that one entity is a fashion-related product or accessory associated with, used by, or worn by another entity.
-
E.
shoppingStyle
Indicates the manner or approach an entity typically uses when shopping, such as their preferred methods, habits, or decision-making style.
- F. None of above.
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_69f22484a5f48190b678cd607700bc82 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6807ef98081908c934c38b7448740 |
completed | May 2, 2026, 10:53 p.m. |
| PD | Predicate disambiguation | batch_69f6760216108190bbb708d53a6c2c25 |
completed | May 2, 2026, 10:09 p.m. |
Created at: April 29, 2026, 7:41 p.m.