In this document, we propose a novel Natural Language Generation (NLG) task, namely the hybrid knowledge graph-to-text and text-to-text generation. It aims at enriching the text obtained from a knowledge graph (KG) encoded in the format of the Resource Description Framework (RDF) with relevant information extrapolated from a complementary textual context. This task is particularly useful when dealing with small-sized ontologies on topics for which richer textual resources are available. In order to solve this task, we present a neural system based on a three-step pipeline: pure KG-to-text generation, content selection from the context, and, finally, the combination of the KG’s verbalization and the additional information into a fluent and cohesive textual output.