Triple
T12420708
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Steenokkerzeel |
E296760
|
entity |
| Predicate | sharesBorderWith |
P224
|
FINISHED |
| Object | Kampenhout |
E634394
|
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: Kampenhout | Statement: [Steenokkerzeel, sharesBorderWith, Kampenhout]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kampenhout Context triple: [Steenokkerzeel, sharesBorderWith, Kampenhout]
-
A.
Kampenhout
chosen
Kampenhout is a municipality in the Flemish Brabant province of Belgium, known for its rural character and proximity to Brussels.
-
B.
Kanegem
Kanegem is a small village in West Flanders, Belgium, known for its historic church and rural character.
-
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.
Bezuidenhout
Bezuidenhout is a neighborhood in The Hague, Netherlands, known for its residential character and proximity to major government and business districts.
-
E.
Molenschot
Molenschot is a small village in the Dutch province of North Brabant, known for its rural character and traditional local community.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d6efd748190a5d9396a343e41e1 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68ea3ec588190bca355953267578f |
completed | May 2, 2026, 11:54 p.m. |
Created at: April 8, 2026, 9:55 p.m.