  The growing trend of Large Language Models (LLM) development has attracted
significant attention, with models for various applications emerging
consistently. However, the combined application of Large Language Models with
semantic technologies for reasoning and inference is still a challenging task.
This paper analyzes how the current advances in foundational LLM, like ChatGPT,
can be compared with the specialized pretrained models, like REBEL, for joint
entity and relation extraction. To evaluate this approach, we conducted several
experiments using sustainability-related text as our use case. We created
pipelines for the automatic creation of Knowledge Graphs from raw texts, and
our findings indicate that using advanced LLM models can improve the accuracy
of the process of creating these graphs from unstructured text. Furthermore, we
explored the potential of automatic ontology creation using foundation LLM
models, which resulted in even more relevant and accurate knowledge graphs.
