Triple
T244730
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinot Noir |
E5011
|
entity |
| Predicate | notableRegion |
P22
|
FINISHED |
| Object | Sonoma |
E40387
|
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: Sonoma | Statement: [Pinot Noir, notableRegion, Sonoma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sonoma Context triple: [Pinot Noir, notableRegion, Sonoma]
-
A.
Napa
Napa is a city in Northern California that serves as the commercial and cultural hub of the renowned Napa Valley wine region.
-
B.
Sonoma Valley
Sonoma Valley is a renowned wine-producing region in California celebrated for its vineyards, wineries, and scenic landscapes.
-
C.
Santa Rosa
Santa Rosa is a mid-sized city in Sonoma County known as a cultural and economic hub of California’s wine country.
-
D.
Calistoga
Calistoga is a small resort town in California’s Napa Valley known for its wineries, hot springs, and relaxed, historic charm.
-
E.
Sonoma County, California
chosen
Sonoma County, California is a Northern California region known for its extensive wine country, scenic Pacific coastline, and redwood forests north of the San Francisco Bay Area.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a260c592cc8190bc642fcd248a1f1b |
completed | Feb. 28, 2026, 3:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3f0a0fd6081908b672fe7106a0c74 |
completed | March 1, 2026, 7:54 a.m. |
Created at: Feb. 28, 2026, 2:53 a.m.