Triple
T14696608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dove Real Beauty campaign |
E345178
|
entity |
| Predicate | usesModels |
P107384
|
FINISHED |
| Object | non-professional models |
—
|
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: non-professional models | Statement: [Dove Real Beauty campaign, usesModels, non-professional models]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesModels Context triple: [Dove Real Beauty campaign, usesModels, non-professional models]
-
A.
usesModelsType
chosen
Indicates that one entity employs or relies on a specific type or category of models in its operation or behavior.
-
B.
useOfModel
Indicates that one entity employs, applies, or relies on a particular model for a specific purpose or task.
-
C.
usedByModel
Indicates that something (such as a resource, method, or component) is utilized or consumed by a particular model.
-
D.
usedToModel
Indicates that one entity serves as a model or representation for another entity, typically for purposes of analysis, simulation, or understanding.
-
E.
modeledWith
Indicates that something is represented, simulated, or described using a particular model, method, or modeling technique.
- 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_69d822e4a8c08190a155df736bb7bc13 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deb58855e081908b38f9515db5677f |
completed | April 14, 2026, 9:45 p.m. |
| PD | Predicate disambiguation | batch_69de657c57ec8190ae0b9bb79a514566 |
completed | April 14, 2026, 4:04 p.m. |
Created at: April 10, 2026, 1:28 a.m.