The fine-tuning of generative pre-trained language models (PLMs) on a new task can be impacted by the choice made for representing the inputs and outputs. This article focuses on the linearization process used to structure and represent, as output, facts extracted from text. On a restricted relation extraction (RE) task, we challenged T5 and BART by fine-tuning them on 12 linearizations, including RDF standard syntaxes and variations thereof. Our benchmark covers: the validity of the produced triples, the performance of the model, the training behaviours and the resources needed. We show these PLMs can learn some syntaxes more easily than others, and we identify a promising ``Turtle Light'' syntax supporting the quick and robust learning of the RE task.