Triple
T23478598
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wayang Orang Sriwedari |
E570336
|
entity |
| Predicate | usesMakeup |
P71229
|
FINISHED |
| Object | stylized wayang wong makeup |
—
|
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: stylized wayang wong makeup | Statement: [Wayang Orang Sriwedari, usesMakeup, stylized wayang wong makeup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMakeup Context triple: [Wayang Orang Sriwedari, usesMakeup, stylized wayang wong makeup]
-
A.
makeupType
Indicates the specific kind or category of makeup associated with an entity.
-
B.
includesCosmetics
Indicates that one entity contains or encompasses cosmetic products or items as part of its contents or offerings.
-
C.
hasMakeupEffectsBy
Indicates that the makeup effects for an entity (such as a film or production) are created or supervised by a specified person or team.
-
D.
cosmeticCategory
Indicates that one entity is classified as belonging to a particular cosmetic or beauty product category defined by the other entity.
-
E.
usesStageMakeup
chosen
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
- 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_69e245af8a88819084f2704f6d265a92 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1a74e7e648190b89006dce7d7ce05 |
completed | April 29, 2026, 6:38 a.m. |
| PD | Predicate disambiguation | batch_69f0620ac3608190b36916261ea50f54 |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 6:02 p.m.