Triple
T22461107
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aqua |
E555229
|
entity |
| Predicate | isPartOf |
P10
|
FINISHED |
| Object | Danone Waters division |
—
|
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: Danone Waters division | Statement: [Aqua, isPartOf, Danone Waters division]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danone Waters division Context triple: [Aqua, isPartOf, Danone Waters division]
-
A.
Nestlé Waters
Nestlé Waters is the bottled water division of Nestlé, responsible for producing and marketing a wide portfolio of still and sparkling water brands worldwide.
-
B.
Danone
chosen
Danone is a multinational French food-products corporation best known for its dairy, plant-based, and bottled water brands.
-
C.
Vittel
Vittel is a French spa town renowned for its mineral water springs and bottled water brand, located in northeastern France.
-
D.
San Pellegrino
San Pellegrino is an Italian brand best known for its naturally carbonated mineral water and flavored sparkling beverages.
-
E.
Gala Water
Gala Water is a river in the Scottish Borders that flows through the town of Galashiels before joining the River Tweed.
- 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_69e11e51fdec8190adfdf9f8a6362221 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15b7f74948190beaf6ea24ba29276 |
completed | April 29, 2026, 1:14 a.m. |
Created at: April 16, 2026, 8:48 p.m.