This paper presents a novel method for creating relation extraction data for low-resource languages. Relation extraction (RE) is a task in natural language processing that involves identifying and extracting meaningful relationships between entities in text. Despite the increasing need to extract relationships from unstructured text, the limited availability of annotated data in low-resource languages presents a significant challenge to the development of high-quality relation extraction models. Our method leverages existing methods for high-resource languages to create training data for low-resource languages. The proposed method is simple, efficient and has the potential to significantly improve the performance of relation extraction models for low-resource languages, making it a promising avenue for future research.