Proper taxonomic identification is essential for biological research. Unfortunately, there are no clear guidelines for taxonomic assignment above the species level. Here, we present a novel approach—GBTD—to the use of genetic divergence to evaluate the taxonomic position of certain samples with simultaneous estimation of the current systematics correctness. This approach includes measuring the raw and model-adjusted distances between DNA sequences and attributing them to the lowest taxonomic levels that are common in sample pairs to reveal distance distributions matching different taxonomic levels (species, genus, family etc.). GBTD facilitated the reassessment of the taxonomic position of the samples, whose genetic distances relative to other samples in the dataset did not match their taxonomic divergence. A data set of complete mitochondrial genome sequences of segmented worms was chosen to test this approach. As a result, numerous inconsistencies in the systematics of samples from GenBank were pointed out. These inconsistencies included both the oversplitting and overlumping of individuals into taxa of different levels and clear cases of misidentification. Our approach sparks re-evaluation of the current systematics where traditional methods fail to provide sufficient resolution.