Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for event sub-sequences based on \emph{temporal relations} such as “directly/eventually-follows”, it does not support querying multi-dimensional event data of multiple related entities. Relational databases allow storing multi-dimensional event data but existing query languages do not support querying for sequences or paths of events defined by temporal relations. In this paper, we report on an exploratory case study to store multi-dimensional event data in labeled property graphs and to query the graphs for structural and temporal properties together. Our main finding is that event data over multiple entities and identifiers with complex relationships can be stored in graph databases in a systematic way. Typical and advanced queries over such multi-dimensional event data can be formulated in the query language Cypher and can be executed efficiently, giving rise to several new research questions.