Triple
T19987145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Valdelsa |
E493962
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Chianti |
—
|
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: Chianti | Statement: [Valdelsa, locatedNear, Chianti]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chianti Context triple: [Valdelsa, locatedNear, Chianti]
-
A.
Chianti
chosen
Chianti is a renowned Italian red wine region in Tuscany, famous for its Sangiovese-based wines and picturesque rolling vineyards.
-
B.
Lambrusco wine
Lambrusco wine is a lightly sparkling Italian red wine, typically fruity and refreshing, traditionally produced in the Emilia-Romagna region.
-
C.
Vernaccia di San Gimignano wine
Vernaccia di San Gimignano wine is a historic Italian white wine, prized for its crisp, mineral character and produced around the medieval town of San Gimignano in Tuscany.
-
D.
Brunello di Montalcino
Brunello di Montalcino is a prestigious Italian red wine made from Sangiovese grapes around the town of Montalcino in Tuscany, renowned for its longevity, structure, and complex flavors.
-
E.
Carmignano
Carmignano is a historic Tuscan town in central Italy renowned for its wine production and scenic hillside landscapes.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fdd1a5c8190af756632aac38bf4 |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:29 p.m.