Triple
T19663483
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China poblana |
E472140
|
entity |
| Predicate | typicalSkirtType |
P116516
|
FINISHED |
| Object | full skirt |
—
|
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: full skirt | Statement: [China poblana, typicalSkirtType, full skirt]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSkirtType Context triple: [China poblana, typicalSkirtType, full skirt]
-
A.
hasSkirtType
chosen
Indicates that an entity is associated with or characterized by a specific type or style of skirt.
-
B.
typicalHemlineLength
Indicates the usual or characteristic length of the hemline associated with an item, style, or category of clothing.
-
C.
typicalFit
Indicates that one entity is a usual, expected, or characteristic match or correspondence for another in a given context.
-
D.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
E.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to 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_69d8e514f2e08190ba70a4449519d218 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6416671fc81908e25b0477234fa0f |
completed | April 20, 2026, 3:08 p.m. |
| PD | Predicate disambiguation | batch_69e514e941008190898d978d7bde91e4 |
completed | April 19, 2026, 5:46 p.m. |
Created at: April 10, 2026, 1:45 p.m.