  Spatial reasoning over text is challenging as the models not only need to
extract the direct spatial information from the text but also reason over those
and infer implicit spatial relations. Recent studies highlight the struggles
even large language models encounter when it comes to performing spatial
reasoning over text. In this paper, we explore the potential benefits of
disentangling the processes of information extraction and reasoning in models
to address this challenge. To explore this, we design various models that
disentangle extraction and reasoning(either symbolic or neural) and compare
them with state-of-the-art(SOTA) baselines with no explicit design for these
parts. Our experimental results consistently demonstrate the efficacy of
disentangling, showcasing its ability to enhance models' generalizability
within realistic data domains.
