Triple
T20102806
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Schwerte |
E496588
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Arnsberg |
—
|
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: Arnsberg | Statement: [Schwerte, region, Arnsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arnsberg Context triple: [Schwerte, region, Arnsberg]
-
A.
Arnsberg
chosen
Arnsberg is a historic town in the Sauerland region of North Rhine-Westphalia, Germany, known for its medieval old town and surrounding forested hills.
-
B.
Meppen
Meppen is a historic town in Lower Saxony, Germany, known as a regional center in the Emsland district near the Dutch border.
-
C.
Nienburg
Nienburg is a historic town in Lower Saxony, Germany, known for its medieval architecture and scenic location along the Weser River.
-
D.
Dorsten
Dorsten is a town in North Rhine-Westphalia, Germany, located in the Ruhr area and known for its mix of industrial heritage and nearby natural landscapes.
-
E.
Nordhorn
Nordhorn is a town in Lower Saxony, Germany, known as the administrative center of the Grafschaft Bentheim district near the Dutch border.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6667170a4819085d07a4188ded541 |
completed | April 20, 2026, 5:46 p.m. |
Created at: April 11, 2026, 11:27 p.m.