Triple
T15043841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SR.N1 |
E379170
|
entity |
| Predicate | hasSkirtType |
P116516
|
FINISHED |
| Object | peripheral air jet (early design without flexible 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: peripheral air jet (early design without flexible skirt) | Statement: [SR.N1, hasSkirtType, peripheral air jet (early design without flexible skirt)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSkirtType Context triple: [SR.N1, hasSkirtType, peripheral air jet (early design without flexible skirt)]
-
A.
hasGarment
Indicates that one entity possesses, wears, or is associated with a particular garment.
-
B.
garmentType
Indicates the specific kind or category of garment associated with an entity.
-
C.
hasApronType
Indicates that an entity is associated with or characterized by a specific type or category of apron.
-
D.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
E.
coatCharacteristic
Indicates that one entity has a particular property, feature, or quality that characterizes its outer covering or surface.
- F. None of above. chosen
Provenance (4 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_69d85cd64d108190853797a95c11cc45 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded82f73208190bb55fa6b20074e27 |
completed | April 15, 2026, 12:13 a.m. |
| PD | Predicate disambiguation | batch_69de9a69d7848190b2b4662dd30f20e9 |
completed | April 14, 2026, 7:50 p.m. |
| PDg | Predicate description generation | batch_69deb1a88d588190996afa8e5b32b552 |
completed | April 14, 2026, 9:29 p.m. |
Created at: April 10, 2026, 3 a.m.