Non-information seeking questions (NISQ) capture the subtle dynamics of human discourse. In this work, we utilize a dataset of over 1,500 information-seeking question(ISQ) and NISQ to evaluate human and machine performance on classifying fine-grained NISQ types. We introduce the first publicly available corpus focused on annotating both ISQs and NISQs as an initial benchmark. Additionally, we establish competitive baselines by assessing diverse systems, including Generative Pre-Trained Transformer Language models, on a new question classification task. Our results demonstrate the inherent complexity of making nuanced NISQ distinctions. The dataset is publicly available at https://github.com/YaoSun0422/NISQ_dataset.git