  Entity resolution, which involves identifying and merging records that refer
to the same real-world entity, is a crucial task in areas like Web data
integration. This importance is underscored by the presence of numerous
duplicated and multi-version data resources on the Web. However, achieving
high-quality entity resolution typically demands significant effort. The advent
of Large Language Models (LLMs) like GPT-4 has demonstrated advanced linguistic
capabilities, which can be a new paradigm for this task. In this paper, we
propose a demonstration system named BoostER that examines the possibility of
leveraging LLMs in the entity resolution process, revealing advantages in both
easy deployment and low cost. Our approach optimally selects a set of matching
questions and poses them to LLMs for verification, then refines the
distribution of entity resolution results with the response of LLMs. This
offers promising prospects to achieve a high-quality entity resolution result
for real-world applications, especially to individuals or small companies
without the need for extensive model training or significant financial
investment.
