Triple
T23258782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karel de Bazel |
E581945
|
entity |
| Predicate | placeOfActivity |
P1527
|
FINISHED |
| Object | Bussum |
—
|
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: Bussum | Statement: [Karel de Bazel, placeOfActivity, Bussum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bussum Context triple: [Karel de Bazel, placeOfActivity, Bussum]
-
A.
Bussum
chosen
Bussum is a town in the province of North Holland in the Netherlands, historically known as a residential and commuter community near Amsterdam.
-
B.
Breckerfeld
Breckerfeld is a small town in North Rhine-Westphalia, Germany, known for its rural character and location in the hilly, forested region of the Sauerland.
-
C.
Bentheim
Bentheim is a historical county in Lower Saxony, Germany, known for its Reformed Protestant heritage and the former County of Bentheim.
-
D.
Hummelsbüttel
Hummelsbüttel is a residential quarter in the borough of Wandsbek in Hamburg, Germany, known for its green spaces and suburban character.
-
E.
Dülmen
Dülmen is a town in western Germany’s North Rhine-Westphalia, known for its location between Münster and the Ruhr area and for the wild Dülmen ponies in the nearby nature reserve.
- 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_69e246079f58819085eaa9c260906880 |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f194c710c48190aff03d210642a043 |
completed | April 29, 2026, 5:19 a.m. |
Created at: April 17, 2026, 4:11 p.m.