Triple
T1629461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cantonese opera |
E35223
|
entity |
| Predicate | hasCostumeStyle |
P5541
|
FINISHED |
| Object | highly stylized historical costume |
—
|
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: highly stylized historical costume | Statement: [Cantonese opera, hasCostumeStyle, highly stylized historical costume]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCostumeStyle Context triple: [Cantonese opera, hasCostumeStyle, highly stylized historical costume]
-
A.
haveDistinctCostume
Indicates that the entities each possess a costume that is different from the others’ costumes.
-
B.
isCostumed
Indicates that an entity is wearing or otherwise adorned with a costume.
-
C.
costumeElement
Indicates that one item functions as a component or part of another item's costume.
-
D.
costumeType
chosen
Indicates the specific kind or category of costume associated with an entity.
-
E.
hasCosmetics
Indicates that one entity possesses, uses, or is associated with cosmetic products or beauty-related items 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_69a886036bc081909ff5de16dbe5e8ea |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9431af5ac8190893133f1ae490142 |
completed | March 5, 2026, 8:47 a.m. |
| PD | Predicate disambiguation | batch_69a907c91c888190b6ed295c1a2e0977 |
completed | March 5, 2026, 4:34 a.m. |
Created at: March 4, 2026, 7:28 p.m.