In this study, we introduce KIALOPRIME, a novel large-scale dataset comprising 5,687 argument discussion graphs with a total of 1,088,801 of supporting, attacking, and neutral argument relations, derived from the structured debates of the online discussion platform Kialo.com. This dataset facilitates in-depth analysis of argument structures and the dynamics of discourse, serving as a substantial resource for computational argumentation research. We explore argument inference through traditional sequence classification and a modern generative reasoning based approach, employing an open-source mixture of experts LLM to interpret and enrich each argument pair with high-quality synthetic elaborations about the argumentative interaction. We achieve baseline results of F1 .899 and .840 within discussions and F1 .908 and .840 across discussions for the argument relation and elaboration classification models, respectively. While the elaboration-based model scores slightly lower on the classification task, we highlight areas of improvement to better capture the hidden complexities of argumentative text. These initial findings are promising as they not only establish robust benchmarks for future studies but also demonstrate the potential for using generative reasoning to provide a more insightful analysis of argument relations.