Triple
T34234057
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saint-Selve |
E878282
|
entity |
| Predicate | regionTypeNearby |
P85695
|
FINISHED |
| Object | wine-growing region |
—
|
LITERAL 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: wine-growing region | Statement: [Saint-Selve, regionTypeNearby, wine-growing region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionTypeNearby Context triple: [Saint-Selve, regionTypeNearby, wine-growing region]
-
A.
nearbyRegionCharacterizedBy
chosen
Indicates that a region located nearby another entity is defined or distinguished by a particular characteristic, feature, or condition.
-
B.
nearbySettlementRegion
Indicates that a settlement is located close to or within the surrounding area of a specified region.
-
C.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
D.
nearbyUrbanCenter
Indicates that one location is geographically close to an urban center, such as a city or large town.
-
E.
nearbyLocation
Indicates that one location is situated close to another location in physical space.
- F. None of above.
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_69f349b22d8c819096b22df268382aa9 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff6a4ce9a08190b98abde3a170dd69 |
completed | May 9, 2026, 5:09 p.m. |
| PD | Predicate disambiguation | batch_69ff69c11634819089d1084bd2c11534 |
completed | May 9, 2026, 5:07 p.m. |
Created at: May 1, 2026, 1:56 a.m.