In version control systems such as Git, concurrent modifications on the same artifacts can cause merge conflicts that may disrupt the development workflow by requiring manual intervention. While research on software merging focused on sophisticated techniques that hardly had any impact in practice, we present an empirical feasibility study on (semi-)automated conflict resolution using a fixed set of only a few language-agnostic conflict resolution patterns. In a large-scale quantitative analysis, we simulate the performance of our hypothetical conflict resolution strategy by classifying the merge conflict resolutions of a diverse sample of 10,000 GitHub projects according to these resolution patterns. We shed light on the derivability of merges on multiple levels of granularity: the conflicting merge commit, its conflicting files, and their individual conflicting chunks. While the derivability rates are promising, they substantially vary between the different levels of granularity. Interestingly, however, by inspecting potential factors affecting derivability, we observe that there are stronger correlations considering individual files than considering the entire merge. A short yet preliminary answer to whether (semi-)automated conflict resolution is easier than we expected is: yes, it might be, particularly if we use the right level of granularity for proposing conflict resolutions. Through our comprehensive analysis, we aspire to bridge the gap between academic innovations on sophisticated merge techniques and real-world merge conflict scenarios, laying the groundwork for more effective and widely accepted automatic merge tools.