Triple
T12697347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rosso Piceno |
E303369
|
entity |
| Predicate | secondaryGrapeVariety |
P11949
|
FINISHED |
| Object | Sangiovese |
E211359
|
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: Sangiovese | Statement: [Rosso Piceno, secondaryGrapeVariety, Sangiovese]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sangiovese Context triple: [Rosso Piceno, secondaryGrapeVariety, Sangiovese]
-
A.
Sangiovese
chosen
Sangiovese is a prominent Italian red wine grape variety best known as the primary component of wines like Chianti and Brunello di Montalcino.
-
B.
Montepulciano
Montepulciano is a historic hilltop town in Tuscany, Italy, renowned for its Renaissance architecture and Vino Nobile wine.
-
C.
Nebbiolo
Nebbiolo is a high-quality red wine grape variety from Italy’s Piedmont region, best known for producing powerful, long-lived wines such as Barolo and Barbaresco.
-
D.
Montepulciano d'Abruzzo
Montepulciano d'Abruzzo is a robust Italian red wine made primarily from the Montepulciano grape in the Abruzzo region, known for its deep color, soft tannins, and approachable, food-friendly character.
-
E.
Merlot
Merlot is a popular red wine grape variety known for producing smooth, medium- to full-bodied wines with soft tannins and flavors of plum, black cherry, and chocolate.
- 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_69d7bdef90d48190b46b88270e780946 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961ed26588190ae76ff17159e06ec |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f684e2292c8190bffb3a8b6e15029c |
completed | May 2, 2026, 11:12 p.m. |
Created at: April 9, 2026, 5:22 p.m.