Triple
T8043461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woman in a Chemise |
E187489
|
entity |
| Predicate | portraysClothing |
P15063
|
FINISHED |
| Object | chemise |
—
|
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: chemise | Statement: [Woman in a Chemise, portraysClothing, chemise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: portraysClothing Context triple: [Woman in a Chemise, portraysClothing, chemise]
-
A.
garmentType
chosen
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.
dressFeature
Indicates that a dress possesses or is characterized by a particular feature, attribute, or design element.
-
D.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
E.
typicallyWornWith
Indicates that one item of clothing or accessory is commonly or customarily worn together with another.
- 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_69ca82b00cb48190b59a300f70e97bd7 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3f4b5e0c819092949af0f995b850 |
completed | March 31, 2026, 3:28 a.m. |
| PD | Predicate disambiguation | batch_69cb049688208190b32088bd2c5930bc |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:23 p.m.