Between 1990 and 2023, Chile’s Congress processed and approved 2738 laws, with an average processing time of 667.8 days from proposal to official publication. Recent political circumstances have underscored the need to identify legislative proposals that can be expedited for approval and which ones are unlikely to be approved at all. This article describes a bottom-up, data-driven classification of voting (and voters) on law proposals, which yield two axis: polarization (lack of agreement on an issue), and (political) alignment (intra-party coincidence of a group’s members regarding certain opinion). And four quadrants: “ideological stance” (high polarization, high alignment), “personal interests” (high polarization, low alignment), “thematic interest” (low polarization, low alignment), and “technical consensus” (low polarization, high alignment). We used this scheme to analyze an existing Open Linked Dataset with semantic web technologies (ontologies, RDF Shape expressions, and URI patterns), which records parliamentarians’ political parties and their voting on law proposals during 1990–2023. We found that most bills (70.14%) are in the technical consensus quadrant, and could have been quickly shepherded to approval. Wider adoption of this analysis to classify new bills may help to speed up their legislative processing, ultimately allowing Congress to serve citizens in a more timely manner.