Triple
T13719207
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jenner family |
E328980
|
entity |
| Predicate | business |
P12567
|
FINISHED |
| Object | Kylie Cosmetics |
E330275
|
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: Kylie Cosmetics | Statement: [Jenner family, business, Kylie Cosmetics]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kylie Cosmetics Context triple: [Jenner family, business, Kylie Cosmetics]
-
A.
Kylie Cosmetics
chosen
Kylie Cosmetics is a makeup and beauty brand founded by Kylie Jenner, known for its trend-setting lip kits and social media–driven marketing.
-
B.
Too Faced
Too Faced is a popular cosmetics brand known for its playful, feminine packaging and trend-driven makeup products.
-
C.
Kylie Skin
Kylie Skin is a skincare line founded by Kylie Jenner offering a range of cleansers, moisturizers, and related beauty products.
-
D.
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.
-
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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd439a121c81908cae964e7756274c |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5c4f848190993f829824eabbef |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.