While the WebNLG dataset has prompted much research on generation from knowledge graphs, little work has examined how well models trained on the WebNLG data generalise to unseen data and work has mostly been focused on English. In this paper, we introduce novel benchmarks for both English and Russian which contain various ratios of unseen entities and properties. These benchmarks also differ from WebNLG in that some of the graphs stem from Wikidata rather than DBpedia. Evaluating various models for English and Russian on these benchmarks shows a strong decrease in performance while a qualitative analysis highlights the various types of errors induced by non i.i.d data.