This study examines large-scale reconciliation of BIBFRAME Work and Hub entities through the matching of over 9,000 MARC records against Library of Congress BIBFRAME datasets. Results show wide variation in matching accuracy, low Hub matching rates, and predominance of one-to-one Work–Instance relationships. Key challenges include difficulties in identifying Hub entities and limitations of current reconciliation workflows. Manual evaluation indicates that reconciliation accuracy is shaped not only by matching algorithms but also by legacy MARC data quality. These findings highlight the need for clearer modeling guidelines, improved data quality, and sufficient computational resources to support reconciliation at scale.