Triple
T25230259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tenkutittu |
E632200
|
entity |
| Predicate | usesMakeupStyle |
P160851
|
FINISHED |
| Object | less heavy facial makeup than Badagutittu |
—
|
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: less heavy facial makeup than Badagutittu | Statement: [Tenkutittu, usesMakeupStyle, less heavy facial makeup than Badagutittu]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMakeupStyle Context triple: [Tenkutittu, usesMakeupStyle, less heavy facial makeup than Badagutittu]
-
A.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
B.
makeupType
Indicates the specific kind or category of makeup associated with an entity.
-
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.
makeupArtist
Indicates that one entity serves as the makeup artist for another, applying or designing cosmetic looks for that entity.
-
E.
includesCosmetics
Indicates that one entity contains or encompasses cosmetic products or items as part of its contents or offerings.
- 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_69e75a8e0f688190a7aebe9a4815e25b |
completed | April 21, 2026, 11:07 a.m. |
| NER | Named-entity recognition | batch_69f60ac643108190ae81561267155791 |
completed | May 2, 2026, 2:31 p.m. |
| PD | Predicate disambiguation | batch_69f602ce79ec8190b8336c2b9de18ac7 |
completed | May 2, 2026, 1:57 p.m. |
| PDg | Predicate description generation | batch_69f606c15af88190958856a9e467b826 |
completed | May 2, 2026, 2:14 p.m. |
Created at: April 21, 2026, 1:04 p.m.