Triple
T15553804
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hailey Bieber |
E370817
|
entity |
| Predicate | hasModeledFor |
P17880
|
FINISHED |
| Object | L'Oréal Professionnel |
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 Professionnel | Statement: [Hailey Bieber, hasModeledFor, L'Oréal Professionnel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: L'Oréal Professionnel Context triple: [Hailey Bieber, hasModeledFor, L'Oréal Professionnel]
-
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.
Biolage
Biolage is a professional haircare brand known for salon-quality products that emphasize botanical ingredients and sustainable practices.
-
C.
Garnier Fructis
Garnier Fructis is a popular hair care brand known for its fruit-based formulas and wide range of shampoos, conditioners, and styling products.
-
D.
Redken
Redken is a professional haircare and hair color brand known for its salon-quality products and innovative, science-driven formulas.
-
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_69d85cc6cf40819091f4a5facee1ebe6 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04a96c0c88190808f68601a36b506 |
completed | April 16, 2026, 2:33 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff456209288190aba6debd434af741 |
completed | May 9, 2026, 2:32 p.m. |
Created at: April 10, 2026, 4:09 a.m.