Triple
T1292546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Active Cosmetics Division |
E27578
|
entity |
| Predicate | parentCompany |
P254
|
FINISHED |
| Object | L'Oréal |
E4816
|
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: L'Oréal | Statement: [Active Cosmetics Division, parentCompany, L'Oréal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L'Oréal Context triple: [Active Cosmetics Division, parentCompany, L'Oréal]
-
A.
L'Oréal
chosen
L'Oréal is a French multinational cosmetics and beauty company recognized as one of the world’s largest and most influential personal care brands.
-
B.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
C.
Elizabeth Arden
Elizabeth Arden was a pioneering Canadian-American businesswoman who founded the Elizabeth Arden cosmetics empire and helped shape the modern beauty industry.
-
D.
Yves Saint Laurent Beauté
Yves Saint Laurent Beauté is a luxury cosmetics and fragrance brand known for its high-end makeup, skincare, and iconic perfumes.
-
E.
Biotherm
Biotherm is a French skincare brand known for its use of aquatic ingredients and scientifically driven formulas for face and body care.
- 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_69a496d4ec448190ad653b2590c46711 |
completed | March 1, 2026, 7:43 p.m. |
| NER | Named-entity recognition | batch_69a4c0f09d5c81909e6dc036fe9c5b4a |
completed | March 1, 2026, 10:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69acde0ffa188190a073650bb595bb16 |
completed | March 8, 2026, 2:25 a.m. |
Created at: March 1, 2026, 7:51 p.m.