Triple
T22414461
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maashorst |
E554080
|
entity |
| Predicate | hasBorderWith |
P224
|
FINISHED |
| Object | Boekel |
—
|
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: Boekel | Statement: [Maashorst, hasBorderWith, Boekel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Boekel Context triple: [Maashorst, hasBorderWith, Boekel]
-
A.
Boekel
chosen
Boekel is a small municipality in the province of North Brabant in the southern Netherlands, known for its rural character and agricultural surroundings.
-
B.
Bekkevoort
Bekkevoort is a municipality in the Flemish Brabant province of Belgium, known for its rural character and agricultural landscape.
-
C.
Groesbeek
Groesbeek is a village in the Dutch province of Gelderland, known for its hilly landscape, World War II history, and wine production.
-
D.
Bergeijk
Bergeijk is a municipality and village in the southern Netherlands, located in the province of North Brabant near the Belgian border.
-
E.
Bennekom
Bennekom is a village in the Dutch province of Gelderland, known for its green surroundings and location between the cities of Ede and Wageningen.
- 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_69e11e4e6ce8819085a1e06d886bf21c |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15945486081908eb3a7b0441c0ef1 |
completed | April 29, 2026, 1:05 a.m. |
Created at: April 16, 2026, 8:46 p.m.