Triple
T17547599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rachel Hunter |
E427368
|
entity |
| Predicate | modeledFor |
P2006
|
FINISHED |
| Object | CoverGirl |
—
|
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: CoverGirl | Statement: [Rachel Hunter, modeledFor, CoverGirl]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CoverGirl Context triple: [Rachel Hunter, modeledFor, CoverGirl]
-
A.
CoverGirl
chosen
CoverGirl is a major American cosmetics brand known for its mass-market makeup products and high-profile celebrity spokesmodels.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
Beauty Inc
Beauty Inc is a beauty-focused media brand owned by Penske Media Corporation, covering trends, news, and analysis in the global cosmetics and personal care industry.
-
D.
Rimmel
Rimmel is a British cosmetics brand best known for its affordable makeup products and the slogan "Get the London Look."
-
E.
Kylie Cosmetics
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
- 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_69d889df6dc081908f67dbadc03c07ee |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e454626cfc8190a2602ba4934b8e6d |
completed | April 19, 2026, 4:04 a.m. |
Created at: April 10, 2026, 5:49 a.m.