Triple
T14495605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arnsberg |
E359488
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object | Alt-Arnsberg |
E359488
|
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: Alt-Arnsberg | Statement: [Arnsberg, hasSubdivision, Alt-Arnsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alt-Arnsberg Context triple: [Arnsberg, hasSubdivision, Alt-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.
Arnstorf
Arnstorf is a market town in the district of Rottal-Inn in Lower Bavaria, Germany, known for its rural character and regional commerce.
-
C.
Hangelsberg
Hangelsberg is a village in the German state of Brandenburg, known as a district of the municipality Grünheide (Mark) in the Oder-Spree region.
-
D.
Sendenhorst
Sendenhorst is a small town in the German state of North Rhine-Westphalia, known for its rural character and location in the Münsterland region.
-
E.
Ahrensburg
Ahrensburg is a town in northern Germany’s Schleswig-Holstein state, known for its historic castle and proximity to Hamburg.
- 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_69d8279740308190af9df93a3af8592e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de93109cb081909a6e846db23a4635 |
completed | April 14, 2026, 7:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd6d9731588190b27a826582e5fc6d |
completed | May 8, 2026, 4:59 a.m. |
Created at: April 10, 2026, 1:21 a.m.