Triple
T2057823
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorpe Dam |
E45716
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | 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: Arnsberg | Statement: [Sorpe Dam, nearCity, Arnsberg]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Arnsberg Context triple: [Sorpe Dam, nearCity, 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.
Lemgo
Lemgo is a historic town in the Lippe district of North Rhine-Westphalia, Germany, known for its well-preserved medieval architecture and Hanseatic heritage.
-
E.
Lüdenscheid
Lüdenscheid is a town in western Germany’s Sauerland region, historically noted for its role in World War II and known today for its metal and plastics industries.
- 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_69a8891a19508190a12ef1e192308dcb |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abb9ae0130819089f7d62005466a45 |
completed | March 7, 2026, 5:37 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b367d99b548190981f471e167198da |
completed | March 13, 2026, 1:26 a.m. |
Created at: March 4, 2026, 7:40 p.m.