Triple
T6693025
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | André Bettencourt |
E152674
|
entity |
| Predicate | employer |
P7
|
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: [André Bettencourt, employer, L'Oréal]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L'Oréal Context triple: [André Bettencourt, employer, 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.
Estée Lauder Companies
Estée Lauder Companies is a leading global cosmetics and skincare conglomerate that owns numerous prestigious beauty brands across makeup, fragrance, haircare, and skincare.
-
C.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
D.
Coty Inc.
Coty Inc. is a global beauty company known for its extensive portfolio of cosmetics, skincare, and fragrance brands.
-
E.
Garnier
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
- 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_69c6880687b08190805278b504d1c92c |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6b1955e448190adbfed7dc28f8c52 |
completed | March 27, 2026, 4:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c70af2ede08190821fd599a3bfc879 |
completed | March 27, 2026, 10:55 p.m. |
Created at: March 27, 2026, 2:05 p.m.