Triple
T9051342
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tuồng |
E216888
|
entity |
| Predicate | makeupFunction |
P78811
|
FINISHED |
| Object | indicate character type |
—
|
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: indicate character type | Statement: [Tuồng, makeupFunction, indicate character type]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: makeupFunction Context triple: [Tuồng, makeupFunction, indicate character type]
-
A.
makeupType
chosen
Indicates the specific kind or category of makeup associated with an entity.
-
B.
makeupArtist
Indicates that one entity serves as the makeup artist for another, applying or designing cosmetic looks for that entity.
-
C.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
D.
metaFunction
Indicates that one function operates on, describes, or manipulates other functions or their behavior at a higher (meta) level.
-
E.
bestMakeupWinner
Indicates that the subject is the winner of an award or recognition for best makeup in a particular context or competition.
- 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_69ca83d362e88190ae44b4e4dc194209 |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cc6b54423081908d9fd985109e336a |
completed | April 1, 2026, 12:48 a.m. |
| PD | Predicate disambiguation | batch_69cc5ee566b081909e3cdaf551dbd0ec |
completed | March 31, 2026, 11:55 p.m. |
Created at: March 30, 2026, 7:10 p.m.