Triple
T675410
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kim Kardashian |
E13066
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
KKW Beauty
KKW Beauty is a cosmetics brand founded by media personality Kim Kardashian, known for its contouring products, neutral-toned palettes, and social media–driven marketing.
|
E83326
|
NE FINISHED |
How this triple was built (4 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: KKW Beauty | Statement: [Kim Kardashian, notableWork, KKW Beauty]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: KKW Beauty Context triple: [Kim Kardashian, notableWork, KKW Beauty]
-
A.
Kiehl's
Kiehl's is an American skincare and cosmetics brand known for its apothecary-style stores and science-driven formulations.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
CoverGirl
CoverGirl is a major American cosmetics brand known for its mass-market makeup products and high-profile celebrity spokesmodels.
-
D.
Redken
Redken is a professional haircare and hair color brand known for its salon-quality products and innovative, science-driven formulas.
-
E.
Revlon
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: KKW Beauty Triple: [Kim Kardashian, notableWork, KKW Beauty]
Generated description
KKW Beauty is a cosmetics brand founded by media personality Kim Kardashian, known for its contouring products, neutral-toned palettes, and social media–driven marketing.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: KKW Beauty Target entity description: KKW Beauty is a cosmetics brand founded by media personality Kim Kardashian, known for its contouring products, neutral-toned palettes, and social media–driven marketing.
-
A.
Kiehl's
Kiehl's is an American skincare and cosmetics brand known for its apothecary-style stores and science-driven formulations.
-
B.
Maybelline New York
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
C.
CoverGirl
CoverGirl is a major American cosmetics brand known for its mass-market makeup products and high-profile celebrity spokesmodels.
-
D.
Redken
Redken is a professional haircare and hair color brand known for its salon-quality products and innovative, science-driven formulas.
-
E.
Revlon
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
- F. None of above. chosen
Provenance (5 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a0266e7c8190a94c4b4b761c59f4 |
completed | March 1, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a5c3a1b6588190b0c9215afb3a9200 |
completed | March 2, 2026, 5:06 p.m. |
| NEDg | Description generation | batch_69a5c7ff50088190827743f3f42622ce |
completed | March 2, 2026, 5:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5cd1dd7848190a987276500040a4f |
completed | March 2, 2026, 5:47 p.m. |
Created at: March 1, 2026, 7:36 p.m.