Triple
T21723536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Let's Get Ready |
E536219
|
entity |
| Predicate | featuresArtist |
P1952
|
FINISHED |
| Object | Nivea |
—
|
NE NERFINISHED |
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: Nivea | Statement: [Let's Get Ready, featuresArtist, Nivea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nivea Context triple: [Let's Get Ready, featuresArtist, Nivea]
-
A.
Nivea
chosen
Nivea is an American R&B singer best known for her early-2000s hits like "Don't Mess with My Man" and collaborations with prominent hip-hop artists.
-
B.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
-
C.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
D.
Jergens
Jergens is a surname most notably associated with American actress Diane Jergens.
-
E.
Mustela
Mustela is a genus of small to medium-sized carnivorous mammals that includes weasels, stoats, ferrets, and related species found across much of the world.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd97150c08190861bdc35416665a9 |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:48 p.m.