Triple
T19663484
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | China poblana |
E472140
|
entity |
| Predicate | typicalSkirtLength |
P101889
|
FINISHED |
| Object | ankle-length |
—
|
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: ankle-length | Statement: [China poblana, typicalSkirtLength, ankle-length]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSkirtLength Context triple: [China poblana, typicalSkirtLength, ankle-length]
-
A.
typicalHemlineLength
chosen
Indicates the usual or characteristic length of the hemline associated with an item, style, or category of clothing.
-
B.
hasSkirtType
Indicates that an entity is associated with or characterized by a specific type or style of skirt.
-
C.
typicalLength
Indicates the usual or characteristic length associated with an entity or phenomenon.
-
D.
typicalFit
Indicates that one entity is a usual, expected, or characteristic match or correspondence for another in a given context.
-
E.
estimatedHeightAtHipsInMeters
Indicates the estimated vertical height, measured in meters, of an entity at the level of its hips.
- 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.