Triple
T19917711
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jockey Club Gold Cup |
E478706
|
entity |
| Predicate | notableWinner |
P2766
|
FINISHED |
| Object | Vino Rosso |
—
|
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: Vino Rosso | Statement: [Jockey Club Gold Cup, notableWinner, Vino Rosso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vino Rosso Context triple: [Jockey Club Gold Cup, notableWinner, Vino Rosso]
-
A.
Vino Rosso
chosen
Vino Rosso is an American Thoroughbred racehorse best known for winning the 2019 Breeders’ Cup Classic and being a top-level dirt router.
-
B.
Chianti
Chianti is a renowned Italian red wine region in Tuscany, famous for its Sangiovese-based wines and picturesque rolling vineyards.
-
C.
Lambrusco wine
Lambrusco wine is a lightly sparkling Italian red wine, typically fruity and refreshing, traditionally produced in the Emilia-Romagna region.
-
D.
Vino
Vino is a VNC-compatible remote desktop server for the GNOME desktop environment on Unix-like systems.
-
E.
Bardolino
Bardolino is a picturesque Italian town in the Veneto region, renowned for its lakeside setting on Lake Garda and its namesake red wine.
- 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_69d8e521855c8190b41871700afc8d6a |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65995bd60819097cfad003dd29731 |
completed | April 20, 2026, 4:51 p.m. |
Created at: April 10, 2026, 1:53 p.m.