  Advances in large language models have notably enhanced the efficiency of
information extraction from unstructured and semi-structured data sources. As
these technologies become integral to various applications, establishing an
objective measure for the quality of information extraction becomes imperative.
However, the scarcity of labeled data presents significant challenges to this
endeavor. In this paper, we introduce an automatic framework to assess the
quality of the information extraction/retrieval and its completeness. The
framework focuses on information extraction in the form of entity and its
properties. We discuss how to handle the input/output size limitations of the
large language models and analyze their performance when extracting the
information. In particular, we introduce scores to evaluate the quality of the
extraction and provide an extensive discussion on how to interpret them.
