Narratives wield a profound influence, shaping perceptions, beliefs, and decision-making processes. Although contemporary pre-trained language models have showcased impressive capabilities in text generation and question-answering tasks, they grapple with inherent limitations in knowledge coverage and exhibit vulnerability to societal biases. This work endeavors to forge a methodology that applies Knowledge Graphs in narrative construction. Rather than solely focusing on fundamental aspects such as the 4W (who, what, when, where) and general relationships, our approach comprises finely detailed semantic relations, delineating precise type of causality such as an event preventing, intending-to-cause, causing, or enabling another event. Applying state-of-art methods to predict such rich information, we demonstrate that it is possible to obtain automatically generated narratives of better grammatical and semantic accuracy.
