Triple
T15472609
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dennit Racing |
E376700
|
entity |
| Predicate | hasSponsor |
P35686
|
FINISHED |
| Object | Perrier |
E376704
|
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: Perrier | Statement: [Dennit Racing, hasSponsor, Perrier]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Perrier Context triple: [Dennit Racing, hasSponsor, Perrier]
-
A.
Perrier
chosen
Perrier is a French brand of naturally carbonated mineral water known for its distinctive green bottles and strong sparkling taste.
-
B.
Vittel
Vittel is a French spa town renowned for its mineral water springs and bottled water brand, located in northeastern France.
-
C.
Schweppes
Schweppes is a historic beverage brand best known for its carbonated soft drinks and tonic waters, now owned and marketed in many regions by The Coca-Cola Company.
-
D.
Volvic
Volvic is a French town in the Auvergne region best known worldwide for its natural mineral water sourced from volcanic springs.
-
E.
Jupiler
Jupiler is a popular Belgian pilsner beer brand widely known for its strong association with football and major sports sponsorships.
- 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_69d85cd21dcc81908646251b1c26ea00 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e03f6c57308190b4cfe661c26addd4 |
completed | April 16, 2026, 1:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff2d075e64819097061ef4c205577e |
completed | May 9, 2026, 12:48 p.m. |
Created at: April 10, 2026, 3:33 a.m.