Triple
T16004161
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rihanna |
E388168
|
entity |
| Predicate | knownFor |
P22
|
FINISHED |
| Object | Fenty Beauty |
E85465
|
NE 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: Fenty Beauty | Statement: [Rihanna, knownFor, Fenty Beauty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fenty Beauty Context triple: [Rihanna, knownFor, Fenty Beauty]
-
A.
Fenty
chosen
Fenty is the surname of global music and fashion icon Rihanna, which she also uses as the brand name for her beauty and fashion ventures.
-
B.
MAC Cosmetics
MAC Cosmetics is a globally recognized professional makeup brand known for its wide range of high-quality cosmetics, trend-setting collaborations, and strong presence in the fashion and beauty industries.
-
C.
Too Faced
Too Faced is a popular cosmetics brand known for its playful, feminine packaging and trend-driven makeup products.
-
D.
Tom Ford Beauty
Tom Ford Beauty is a luxury cosmetics and fragrance brand known for its high-end makeup, skincare, and signature scents created under the Tom Ford fashion label.
-
E.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157fe776c81908f7bf29ef064a6ba |
completed | April 16, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe470cd5881909cc0c48b3a540d61 |
completed | May 10, 2026, 1:50 a.m. |
Created at: April 10, 2026, 4:55 a.m.