Triple
T30147053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | No. 2 Service Dress |
E766283
|
entity |
| Predicate | hasTrousersType |
P15063
|
FINISHED |
| Object | straight-leg trousers |
—
|
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: straight-leg trousers | Statement: [No. 2 Service Dress, hasTrousersType, straight-leg trousers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTrousersType Context triple: [No. 2 Service Dress, hasTrousersType, straight-leg trousers]
-
A.
hasSkirtType
Indicates that an entity is associated with or characterized by a specific type or style of skirt.
-
B.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
-
C.
garmentType
chosen
Indicates the specific kind or category of garment associated with an entity.
-
D.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
-
E.
hasApronType
Indicates that an entity is associated with or characterized by a specific type or category of apron.
- 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_69f2247909048190ae86c2160cf8b566 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69fe5d58c3e48190910aa3c23485e2c4 |
completed | May 8, 2026, 10:02 p.m. |
| PD | Predicate disambiguation | batch_69fe5c92090c8190bcfa412c0a3619df |
completed | May 8, 2026, 9:58 p.m. |
Created at: April 29, 2026, 7:19 p.m.