Record linkage across diverse domains is a challenging task with industrial applications ranging from medical records to social media identity linkage. In this study, we present a complex case in the music industry: linking incomplete metadata across domains, from Sound Recordings (SRs) to their corresponding Musical Works (WKs). We present a definition of the problem and highlight its key aspects: comparing record fields beyond conventional string similarity; matching lists of names that only partially align; applying attribute rules, as some attribute values may reflect the quality of information; and applying contextual rules, since the match between an SR and a WK should be evaluated within the context consisting of related WKs. We present a synthetic benchmark that replicates the complexities of the real-world industry problems. While not the focus of the paper, we also report preliminary results of a Transformer-based model that leverages pre-trained embeddings of entity attribute values along with information from the aforementioned key aspects.