The representation of scenarios drawn from real-world domains is certainly favored by the presence of simple but powerful languages capable of capturing all facets of the problem. Probabilistic Logic Programming (PLP) [5,11] plays a fundamental role in this thanks to its ability to represent uncertain and complex information [3,10] and the possibility to be integrated in sub-symbolic systems to help the explainability of the models [9]. Several probabilistic logic programming languages based on the so-called distribution semantics [14] have been proposed over the years, among which are PRISM [14], Logic Programs with Annotated Disjunctions (LPADs) [15], and ProbLog [6].