  Recent work has shown the capability of Large Language Models (LLMs) to solve
tasks related to Knowledge Graphs, such as Knowledge Graph Completion, even in
Zero- or Few-Shot paradigms. However, they are known to hallucinate answers, or
output results in a non-deterministic manner, thus leading to wrongly reasoned
responses, even if they satisfy the user's demands. To highlight opportunities
and challenges in knowledge graphs-related tasks, we experiment with three
distinguished LLMs, namely Mixtral-8x7b-Instruct-v0.1, GPT-3.5-Turbo-0125 and
GPT-4o, on Knowledge Graph Completion for static knowledge graphs, using
prompts constructed following the TELeR taxonomy, in Zero- and One-Shot
contexts, on a Task-Oriented Dialogue system use case. When evaluated using
both strict and flexible metrics measurement manners, our results show that
LLMs could be fit for such a task if prompts encapsulate sufficient information
and relevant examples.
