Datasets that pair Information Graphs (KG) and textual content collectively (KG-T) can be utilized to coach ahead and reverse neural fashions that generate textual content from KG and vice versa. Nonetheless fashions educated on datasets the place KG and textual content pairs will not be equal can undergo from extra hallucination and poorer recall. On this paper, we confirm this empirically by producing datasets with totally different ranges of noise and discover that noisier datasets do certainly result in extra hallucination. We argue that the flexibility of ahead and reverse fashions educated on a dataset to cyclically regenerate supply KG or textual content is a proxy for the equivalence between the KG and the textual content within the dataset. Utilizing cyclic analysis we discover that manually created WebNLG is a lot better than routinely created TeKGen and T-REx. Knowledgeable by these observations, we assemble a brand new, improved dataset referred to as LAGRANGE utilizing heuristics meant to enhance equivalence between KG and textual content and present the influence of every of the heuristics on cyclic analysis. We additionally assemble two artificial datasets utilizing massive language fashions (LLMs), and observe that these are conducive to fashions that carry out considerably nicely on cyclic era of textual content, however much less so on cyclic era of KGs, in all probability due to a scarcity of a constant underlying ontology.