Triple
T18517741
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Garnier |
E452508
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Garnier |
—
|
NE NERFINISHED |
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: Garnier | Statement: [Charles Garnier, familyName, Garnier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Garnier Context triple: [Charles Garnier, familyName, Garnier]
-
A.
Garnier
chosen
Garnier is a French surname most famously associated with architect Charles Garnier, designer of the Paris Opéra.
-
B.
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.
-
C.
Nivea
Nivea is an American R&B singer best known for her early-2000s hits like "Don't Mess with My Man" and collaborations with prominent hip-hop artists.
-
D.
Mustela
Mustela is a genus of small to medium-sized carnivorous mammals that includes weasels, stoats, ferrets, and related species found across much of the world.
-
E.
Neutrogena
Neutrogena is a widely recognized skincare and cosmetics brand known for its dermatologist-recommended products, including facial cleansers, moisturizers, sunscreens, and acne treatments.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d386df84819092355ebb260d848e |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5338b2cd0819095db59f6bfc70814 |
completed | April 19, 2026, 7:56 p.m. |
Created at: April 10, 2026, 11:36 a.m.