Cultural artifact classification supports preservation, scholarship, and anti-trafficking enforcement, but computational approaches face severe data scarcity: limited labeled collections, inconsistent metadata, and complex annotation requirements. We present a methodology for large-scale dataset creation from aggregated heritage platforms that combines cultural marker extraction from unstructured text descriptions, cross-lingual querying across multiple European languages, visual clustering using self-supervised features, and collection-aware data splitting. Applied to Europeana, this produced datasets ranging from 33,859 to 310,252 objects across 6 to 62 classes of varying granularity. Visual clustering removed 34% of noisy content while enabling fine-grained subcategories. A k-nearest-neighbor probe of the resulting feature space revealed that 79.5% of nearest neighbors share the same institutional source. This nearly matches the 83.0% same-civilization rate, indicating that institutional signatures are almost as dominant as cultural content. We quantify this bias per civilization and propose collection-aware splitting as a necessary countermeasure for realistic evaluation.