Triple
T7507285
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ede-Wageningen railway station |
E177421
|
entity |
| Predicate | hasNearbyRoadConnection |
P11435
|
FINISHED |
| Object | A12 motorway |
—
|
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: A12 motorway | Statement: [Ede-Wageningen railway station, hasNearbyRoadConnection, A12 motorway]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNearbyRoadConnection Context triple: [Ede-Wageningen railway station, hasNearbyRoadConnection, A12 motorway]
-
A.
hasNearbyStreet
Indicates that one entity is located close to or adjacent to a street.
-
B.
hasConnectingStreet
Indicates that two locations are linked by a street that directly connects them.
-
C.
linkedByRoadTo
chosen
Indicates that two locations are directly connected to each other by a road suitable for travel.
-
D.
hasConnectingRoadNumber
Indicates that there exists a road connection between two locations or road segments identified by a specific road number.
-
E.
hasLocalRoad
Indicates that there exists a local road connection or association between the related entities.
- 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_69c69f276b108190af2cc790b6554544 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b76a288190bb3608a5e3bfa212 |
completed | March 27, 2026, 9:25 p.m. |
| PD | Predicate disambiguation | batch_69c6f4d44e9481909813e073b194f6f4 |
completed | March 27, 2026, 9:21 p.m. |
Created at: March 27, 2026, 3:45 p.m.