Triple
T19453615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | TRANSform Me |
E486678
|
entity |
| Predicate | hasMakeoverElement |
P23574
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [TRANSform Me, hasMakeoverElement, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMakeoverElement Context triple: [TRANSform Me, hasMakeoverElement, true]
-
A.
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.
-
B.
hasMade
Indicates that an entity has created, produced, or brought about another entity or outcome.
-
C.
hasCosmetics
chosen
Indicates that one entity possesses, uses, or is associated with cosmetic products or beauty-related items in relation to another entity or context.
-
D.
usesStageMakeup
Indicates that one entity applies or wears theatrical or stage makeup in relation to another entity or context.
-
E.
hasDecor
Indicates that one entity possesses, features, or is adorned with a particular decorative element or style.
- 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_69d8e8d86d608190bd199a98d0297f27 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6339407a08190a3e0213bfbb4df3d |
completed | April 20, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7499a4819082bec0be8afba35c |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:38 p.m.