Question Answering (QA) over Knowledge Graphs (KG) has the aim of developing a system that is capable of answering users' questions using the information coming from one or multiple Knowledge Graphs, like DBpedia, Wikidata and so on. Question Answering systems need to translate the question of the user, written using natural language, into a query formulated through a specific data query language that is compliant with the underlying KG. This translation process is already non-trivial when trying to answer simple questions that involve a single triple pattern and becomes even more troublesome when trying to cope with questions that require the presence of modifiers in the final query, i.e. aggregate functions, query forms, and so on. The attention over this last aspect is growing but has never been thoroughly addressed by the existing literature. Starting from the latest advances in this field, we want to make a further step towards this direction. The aim of this work is to provide a publicly available dataset designed for evaluating the performance of a QA system in translating articulated questions into a specific data query language. This dataset has also been used to evaluate three QA systems available at the state of the art.