  We present MobIE, a German-language dataset, which is human-annotated with 20
coarse- and fine-grained entity types and entity linking information for
geographically linkable entities. The dataset consists of 3,232 social media
texts and traffic reports with 91K tokens, and contains 20.5K annotated
entities, 13.1K of which are linked to a knowledge base. A subset of the
dataset is human-annotated with seven mobility-related, n-ary relation types,
while the remaining documents are annotated using a weakly-supervised labeling
approach implemented with the Snorkel framework. To the best of our knowledge,
this is the first German-language dataset that combines annotations for NER, EL
and RE, and thus can be used for joint and multi-task learning of these
fundamental information extraction tasks. We make MobIE public at
https://github.com/dfki-nlp/mobie.
