Triple
T2057805
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sorpe Dam |
E45716
|
entity |
| Predicate | operator |
P179
|
FINISHED |
| Object | Ruhrverband |
E229752
|
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: Ruhrverband | Statement: [Sorpe Dam, operator, Ruhrverband]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ruhrverband Context triple: [Sorpe Dam, operator, Ruhrverband]
-
A.
Ruhrverband
chosen
Ruhrverband is a German water management association responsible for operating reservoirs, dams, and water infrastructure in the Ruhr river basin.
-
B.
Ruhr area
The Ruhr area is a major urban and industrial region in western Germany known for its dense concentration of cities, former coal and steel industries, and subsequent transformation into a cultural and service hub.
-
C.
Ruhr
The Ruhr is a river in western Germany that flows through the Ruhr industrial region before joining the Rhine.
-
D.
Rhine-Ruhr metropolitan region
The Rhine-Ruhr metropolitan region is a major polycentric urban and industrial area in western Germany that encompasses several large cities, including Cologne, and forms one of Europe’s largest population and economic centers.
-
E.
Duisburg
Duisburg is a major industrial and port city in western Germany’s Ruhr region, known for its steel production and one of the world’s largest inland harbors.
- 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_69ae271cb26c81908664f4c26f4fb5ea |
completed | March 9, 2026, 1:49 a.m. |
Created at: March 4, 2026, 7:40 p.m.