Knowledge Graphs (KGs) are an essential component of neuro-symbolic AI. KG Embedding Models (KGEMs) are used to represent elements of a KG (its entities and relations) in a vector space, to enable efficient processing and reasoning over knowledge. Most KGEMs are evaluated against datasets derived from the Freebase KG: FB15k and FB15k-237. In this paper, we identify limitations in these datasets with respect to Compound Value Types (CVTs), which are nodes introduced in Freebase as a substitute for \uD835\uDC5B-ary relations. In FB15k and FB51k-237, CVTs have been removed, thereby eliminating valuable information. To evaluate whether KGEMs can learn semantically accurate representations of entities and relations in Freebase, we introduce here a new dataset named FB15k-CVT, which reintroduces the deleted CVT nodes. In a preliminary evaluation, we assess the limitations of baseline KGEMs (TransE, DistMult) in the presence of CVTs. The evaluation suggests that KGEMs based on tensor decomposition are more promising than translational models but, most of all, it calls for further experiments with KGEMs that can answer conjunctive queries or that preserve logical entailment.