  This paper presents a comprehensive exploration of relation extraction
utilizing advanced language models, specifically Chain of Thought (CoT) and
Graphical Reasoning (GRE) techniques. We demonstrate how leveraging in-context
learning with GPT-3.5 can significantly enhance the extraction process,
particularly through detailed example-based reasoning. Additionally, we
introduce a novel graphical reasoning approach that dissects relation
extraction into sequential sub-tasks, improving precision and adaptability in
processing complex relational data. Our experiments, conducted on multiple
datasets, including manually annotated data, show considerable improvements in
performance metrics, underscoring the effectiveness of our methodologies.
