In recent years, many research efforts focused on the generation of Linguistic Linked (Open) Data (LLOD), driven by their potential to enhance Natural Language Processing (NLP) tasks. Researchers across the globe have dedicated substantial efforts to curate, standardise, interconnect, and leverage language resources, pursuing the generation of Linguistic Linked Data and the population of the LLOD cloud. However, the rise of Large Language Models (LLMs) has challenged conventional NLP methodologies, as they are able to better capture complex linguistic and semantic patterns as they employ deep learning architectures trained on vast amounts of textual data. Therefore, this talks presents three main objectives: first, to give an overview of the LLOD cloud status, including latest advances in LLOD-aware technologies and representation models; second,to  identify shortcomings of current LLMs in different NLP tasks, specially, domain oriented; third, to explore the possibilities of incorporating LLOD into LLMs, both for data pre and postprocessing and for knowledge injection into the LLM architecture. Since the latter is still an unexplored territory, the talk intends to identify potential research areas open for discussion.