The digitization of historic architecture increasingly relies on dense point clouds, yet the conversion of these datasets into structured Historic Building Information Models (HBIM) remains slow, inconsistent, and heavily dependent on manual interpretation. This paper introduces a classification-driven, mesh-based semi-automated workflow designed to close this gap by providing a controlled, repeatable path from raw TLS data to BIM-ready geometry. The method combines three elements strategically integrated into a unified framework: (1) pre-classified point cloud groups that establish a structured starting point, (2) mesh simplification and slice-based geometric reconstruction executed through Rhino and Grasshopper, and (3) direct BIM integration using Rhino.Inside.Revit to generate categorized HBIM components rather than passive mesh imports. The workflow is validated on an irregular exterior stone column from the historic chapel in Sopronhorpács, Hungary, an element characterized by surface erosion, asymmetric profiles, and deviations from verticality. This type of geometry typically challenges both manual modeling and fully automated shape-fitting. The proposed method reconstructed the column as a Revit Structural Column element with a substantial reduction in modeling time compared to a manual Scan-to-BIM workflow. A deviations analysis confirmed that the reconstructed geometry remained within the millimeter-level accuracy required for conservation-grade documentation. The study demonstrates that combining element-based classification, mesh preprocessing, and controlled semi-automation can significantly improve both the speed and reliability of Scan-to-HBIM processes without requiring technical expertise yet delivers results that align with the precision expected in scientific documentation. By formalizing the Pre-Classified Modeling Logic (PCML), the approach provides a foundation for reconstructing a wide range of heritage elements and establishes a practical step forward toward more efficient, interpretable, and accessible digital preservation practices.