Triple
T18576336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Vyartsilya |
E453996
|
entity |
| Predicate | hasBorderCheckpointNearby |
P49266
|
FINISHED |
| Object | Russian–Finnish border crossing |
—
|
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: Russian–Finnish border crossing | Statement: [Vyartsilya, hasBorderCheckpointNearby, Russian–Finnish border crossing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasBorderCheckpointNearby Context triple: [Vyartsilya, hasBorderCheckpointNearby, Russian–Finnish border crossing]
-
A.
hasNearbyBoundary
Indicates that one entity’s boundary lies close to, but does not necessarily touch or coincide with, the boundary of another entity.
-
B.
hasBorderThrough
Indicates that a border between two regions or entities passes through or along a specified intermediate area, feature, or object.
-
C.
borderStateNearby
Indicates that one state is geographically close to, but does not necessarily directly touch, the border of another state.
-
D.
hasNearbyGate
Indicates that one entity has a gate located in close physical proximity to it.
-
E.
hasNearbyCrossingPoint
chosen
Indicates that one location has a crossing point (such as a bridge, crosswalk, or intersection) situated close to it.
- 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_69d8d38974308190a9174430ef256b73 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e543c9d26c8190a80dda411cd0c9ac |
completed | April 19, 2026, 9:06 p.m. |
| PD | Predicate disambiguation | batch_69e478c98d4c81909d37a0e72c6e7bd0 |
completed | April 19, 2026, 6:40 a.m. |
Created at: April 10, 2026, 11:43 a.m.