This study investigates the predicate-argument structure in Korean language processing. Despite the importance of distinguishing mandatory arguments and optional modifiers in sentences, research in this area has been limited. We introduce a dataset with token-level annotations which labels mandatory and optional elements as complements and adjuncts, respectively. Particularly, we reclassify certain Korean phrases, previously misidentified as adverbial phrases, as complements, addressing misuses of the term adjunct in existing Korean treebanks. Utilizing a Korean dependency treebank, we develop an automatic labeling technique for complements and adjuncts. Experiments using the proposed dataset yield satisfying results, demonstrating that the dataset is trainable and reliable.