Understanding how opinions on different issues evolve together is essential for modeling collective intelligence, yet this remains underexplored due to the absence of standardized benchmarks. We introduce the concept of an opinion graph, where nodes represent social opinions on real-world events (e.g., presidential elections, stock predictions) and edges capture pairwise relationships between them. Building on this, we present OpinionBench, a continuously evolving benchmark derived from multiple live prediction markets, designed to evaluate whether large language models (LLMs) can uncover the latent structure within evolving social opinions. OpinionBench employs an scalable and automated graph construction and labeling pipeline, which assigns edge labels using time-series co-movement, semantic similarity, and metadata, followed by human validation. Experiments show that (1) LLMs consistently outperform baselines in identifying opinion correlations across domains, and (2) LLMs can recover the underlying graph structure through edge prediction. OpinionBench provides a challenging testbed for assessing LLMs’ ability to capture complex patterns of social opinion co-evolution.