  Recently, ChatGPT has attracted a lot of interest from both researchers and
the general public. While the performance of ChatGPT in named entity
recognition and relation extraction from Standard English texts is
satisfactory, it remains to be seen if it can perform similarly for Malaysian
English. Malaysian English is unique as it exhibits morphosyntactic and
semantical adaptation from local contexts. In this study, we assess ChatGPT's
capability in extracting entities and relations from the Malaysian English News
(MEN) dataset. We propose a three-step methodology referred to as
\textbf{\textit{educate-predict-evaluate}}. The performance of ChatGPT is
assessed using F1-Score across 18 unique prompt settings, which were carefully
engineered for a comprehensive review. From our evaluation, we found that
ChatGPT does not perform well in extracting entities from Malaysian English
news articles, with the highest F1-Score of 0.497. Further analysis shows that
the morphosyntactic adaptation in Malaysian English caused the limitation.
However, interestingly, this morphosyntactic adaptation does not impact the
performance of ChatGPT for relation extraction.
