Knowledge Graphs (KGs) are a popular means of representing information in a machine readable and interpretable way. This popularity, though, means that KG quality becomes increasingly important. However, most related research considers KGs as static, ignoring their evolutionary aspect. In this work, we focus on Temporal KG (TKG) quality and propose a novel method for detecting inconsistencies within a TKG’s triples by leveraging internal information. Our method involves automatically detecting all temporal relations of a TKG and their different variants, and by leveraging their support and confidence metrics, determining the dominant variant, while labeling as potentially inconsistent all triples following the other variants. We showcase the usability of our approach through a first case study on the YAGO Tiny KG, and discuss potential expansions.