Aggregation query over free text is a long-standing yet underexplored problem. It requires exhaustive evidence collection, where systems must “find all” satisfying entities rather than merely “find one” plausible answer. Existing paradigms are not designed for this completeness requirement, leading to incomplete retrieval and systematic undercounting. This limitation stems from the lack of suitable benchmarks. In this work, we formalize corpus-bounded entity-level aggregation querying over text, where answers must be grounded in the given text corpus. We introduce AGGBench, an aggregation benchmark constructed from finance reports and research papers through a corpus-grounded pipeline with LLM-assisted human validation. AGGBench contains 498 aggregation queries, including both single- and multi-constraint queries with evidence-supported gold answers. Experiments on representative basic RAG, structured and schema-first and agentic methods show that existing systems struggle to preserve evidence coverage and accurately count satisfying entities, highlighting the need for completeness-oriented methods for aggregation over unstructured text. The data and code are available in https://github.com/luck-seu/AGGBench.

Geospatial Entity Resolution (GER) plays a central role in integrating spatial data from diverse sources. However, existing methods are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. While recent advances in Large Language Models (LLMs) offer strong semantic reasoning and zero-shot capabilities, directly applying them to GER remains inadequate due to their limited spatial understanding and high inference cost. In this work, we present GER-LLM, a framework that integrates LLMs into the GER pipeline. To address the challenge of spatial understanding, we design a spatially informed blocking strategy based on adaptive quadtree partitioning and Area of Interest (AOI) detection, preserving both spatial proximity and functional relationships. To mitigate inference overhead, we introduce a group prompting mechanism with graph-based conflict resolution, enabling joint evaluation of diverse candidate pairs and enforcing global consistency across alignment decisions. Extensive experiments on real-world datasets demonstrate the effectiveness of our approach, yielding significant improvements over state-of-the-art methods.