Triple
T37310947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nyonya |
E926202
|
entity |
| Predicate | dressAssociation |
P27860
|
FINISHED |
| Object | kebaya Nyonya |
—
|
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: kebaya Nyonya | Statement: [Nyonya, dressAssociation, kebaya Nyonya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: dressAssociation Context triple: [Nyonya, dressAssociation, kebaya Nyonya]
-
A.
typicallyWornWith
chosen
Indicates that one item of clothing or accessory is commonly or customarily worn together with another.
-
B.
usesDressing
Indicates that one entity applies or employs a particular dressing (such as a sauce, covering, or treatment) in relation to another entity or context.
-
C.
dressFeature
Indicates that a dress possesses or is characterized by a particular feature, attribute, or design element.
-
D.
styleCombination
Indicates a relationship where multiple styles are combined or coordinated to form a unified stylistic configuration or presentation.
-
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_69f76eb1bc508190924e9fa5d8acdeb3 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fb78cbef988190b8f79d946b46e6b2 |
completed | May 6, 2026, 5:22 p.m. |
| PD | Predicate disambiguation | batch_69fb5a9ac5a08190b24ef308963fc52b |
completed | May 6, 2026, 3:13 p.m. |
Created at: May 3, 2026, 4:16 p.m.