Triple
T1325249
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maybelline New York |
E28311
|
entity |
| Predicate | originalName |
P65
|
FINISHED |
| Object | Maybelline |
E28311
|
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: Maybelline | Statement: [Maybelline New York, originalName, Maybelline]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maybelline Context triple: [Maybelline New York, originalName, Maybelline]
-
A.
Maybelline New York
chosen
Maybelline New York is a major American cosmetics and beauty brand known worldwide for its mass-market makeup products.
-
B.
Revlon
Revlon is a major American cosmetics, skincare, fragrance, and personal care company known for its mass-market beauty products and global brand presence.
-
C.
Essie
Essie is a popular nail polish and nail care brand known for its wide range of fashion-forward colors and salon-quality formulas.
-
D.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
E.
CoverGirl
CoverGirl is a major American cosmetics brand known for its mass-market makeup products and high-profile celebrity spokesmodels.
- 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_69a498540a2481909e807a762280d3ba |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c19e81c0819092f85201ae34422a |
completed | March 1, 2026, 10:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acbf2e13dc8190902879be5fa69adb |
completed | March 8, 2026, 12:13 a.m. |
Created at: March 1, 2026, 7:55 p.m.