Triple
T6942255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blue Therapy line |
E160706
|
entity |
| Predicate | ownedBy |
P347
|
FINISHED |
| Object | Biotherm |
E29872
|
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: Biotherm | Statement: [Blue Therapy line, ownedBy, Biotherm]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Biotherm Context triple: [Blue Therapy line, ownedBy, Biotherm]
-
A.
Biotherm
chosen
Biotherm is a French skincare brand known for its use of aquatic ingredients and scientifically driven formulas for face and body care.
-
B.
La Roche-Posay
La Roche-Posay is a French dermatological skincare brand known for its sensitive-skin-friendly formulas developed with thermal spring water and widely recommended by dermatologists.
-
C.
Lancôme
Lancôme is a French luxury cosmetics and skincare brand renowned for its high-end perfumes, makeup, and beauty products.
-
D.
Guerlain
Guerlain is a historic French luxury perfume, cosmetics, and skincare house renowned for its iconic fragrances and high-end beauty products.
-
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_69c6884f3db4819080ad65da69386206 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6da85f43881909549ac26b3db135a |
completed | March 27, 2026, 7:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7942eeb288190acad3bf999a6ea2e |
completed | March 28, 2026, 8:41 a.m. |
Created at: March 27, 2026, 2:28 p.m.