The field of Relation Extraction and its downstream applications are experiencing a considerable surge of attention due to the emergence of Large Language Models (LLMs), shifting the focus from Sentence-level to Document-level Relation Extraction. Despite this, employing LLMs introduces new challenges, such as the need for defining a prompting strategy and measures for controlling hallucinations. Moreover, limited input lengths and performance degradation on large text inputs, even when considerably below the limit, require careful considerations when prompting the model. This work aims to show how different prompting strategies, small changes to text processing, and even the choice of model and nature of the corpora, can yield vastly different results when performing this complex NLP task. In doing so, we highlight the necessity of establishing, assessing, and managing these elements in LLM-based Relation Extraction processes, which would eventually enable defining a baseline system applicable to both general and domain-specific corpora. Furthermore, by understanding these factors, we can enhance the reliability and accuracy of LLM-based Relation Extraction systems, leading to more robust applications across various domains.