Triple
T6254472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Banarasi saris |
E140126
|
entity |
| Predicate | blouseRequirement |
P42160
|
FINISHED |
| Object | usually worn with a matching blouse |
—
|
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: usually worn with a matching blouse | Statement: [Banarasi saris, blouseRequirement, usually worn with a matching blouse]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: blouseRequirement Context triple: [Banarasi saris, blouseRequirement, usually worn with a matching blouse]
-
A.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
B.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
C.
traditionalDressVariant
Indicates that one traditional dress is a variant or localized form of another traditional dress within the same broader cultural or stylistic tradition.
-
D.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
E.
hasGarment
chosen
Indicates that one entity possesses, wears, or is associated with a particular garment.
- 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_69c008b4858c819095b0199114a9a87b |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063625608819081f5422112c80ce5 |
completed | March 22, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69c05605566c81908e197f5accd072d2 |
completed | March 22, 2026, 8:50 p.m. |
Created at: March 22, 2026, 4:24 p.m.