This paper presents a German corpus for Named Entity Linking (NEL) and Knowledge Base Population (KBP) tasks. We describe
the annotation guideline, the annotation process, NIL clustering techniques and conversion to popular NEL formats such as NIF and
TAC that have been used to construct this corpus based on news transcripts from the German regional broadcaster RBB (Rundfunk
Berlin Brandenburg). Since creating such language resources requires significant effort, the paper also discusses how to derive
additional evaluation resources for tasks like named entity contextualization or ontology enrichment by exploiting the links between
named entities from the annotated corpus. The paper concludes with an evaluation that shows how several well-known NEL tools
perform on the corpus, a discussion of the evaluation results, and with suggestions on how to keep evaluation corpora and datasets up to date.