Triple
T15438462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Woman in a Green Jacket (watercolor) |
E369832
|
entity |
| Predicate | subjectClothing |
P99194
|
FINISHED |
| Object | green jacket |
—
|
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: green jacket | Statement: [Woman in a Green Jacket (watercolor), subjectClothing, green jacket]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: subjectClothing Context triple: [Woman in a Green Jacket (watercolor), subjectClothing, green jacket]
-
A.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
B.
colorOfApparel
chosen
Indicates the specific color attribute associated with a piece of apparel or clothing item.
-
C.
fashionStyle
Indicates the characteristic way in which an entity dresses or presents themselves in terms of clothing and appearance.
-
D.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
E.
dressRecommendation
Indicates a suggested or advised choice of dress for a particular person and/or occasion.
- 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_69d85a19180081909925012fbf4e62a3 |
completed | April 10, 2026, 2:02 a.m. |
| NER | Named-entity recognition | batch_69e03edca064819081510bf303271062 |
completed | April 16, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69ded28276f481908c2038bb301e57cf |
completed | April 14, 2026, 11:49 p.m. |
Created at: April 10, 2026, 3:21 a.m.