Triple
T6567470
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Colossal mascara |
E153944
|
entity |
| Predicate | cosmeticCategory |
P71910
|
FINISHED |
| Object | eye 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: eye makeup | Statement: [Colossal mascara, cosmeticCategory, eye makeup]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cosmeticCategory Context triple: [Colossal mascara, cosmeticCategory, eye makeup]
-
A.
makeupArtist
Indicates that one entity serves as the makeup artist for another, applying or designing cosmetic looks for that entity.
-
B.
styleCategory
Indicates the stylistic classification or genre category that an item, work, or entity belongs to.
-
C.
bodyTreatment
Indicates a treatment or therapeutic procedure that is applied to a person's body.
-
D.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
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. 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6cc9c6cb0819084fec8e0beb430de |
completed | March 27, 2026, 6:29 p.m. |
| PD | Predicate disambiguation | batch_69c6acf93cb48190b54f5dd6febd34dc |
completed | March 27, 2026, 4:14 p.m. |
| PDg | Predicate description generation | batch_69c6cc988c0081909d22b86ca299331c |
completed | March 27, 2026, 6:29 p.m. |
Created at: March 27, 2026, 1:53 p.m.