# STIndex: A Context-Aware Multi-Dimensional Spatiotemporal Information Extraction System

arXiv: https://arxiv.org/abs/2604.08597 · HTML: https://mlsanity.com/paper/2604.08597/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 9 references that are in the map.
- First posted on arXiv 7 Apr 2026.

## Built on by (most cited first, 1 of 1)

- [MineTRACE: An Evidence-Grounded Interactive Reasoning System for Mineral Prospectivity](https://mlsanity.com/paper/2609.02060/) (2026)

## Abstract (arXiv)

Extracting structured knowledge from unstructured data still faces practical limitations: entity and event extraction pipelines remain brittle, knowledge graph construction requires costly ontology engineering, and cross-domain generalization is rarely production-ready. In contrast, space and time provide universal contextual anchors that naturally align heterogeneous information and benefit downstream tasks such as retrieval and reasoning. We introduce STIndex, an end-to-end system that structures unstructured content into a multidimensional spatiotemporal data warehouse. Users define domain-specific analysis dimensions with configurable hierarchies, while large language models perform context-aware extraction and grounding. STIndex integrates document-level memory, geocoding correction, and quality validation, and offers an interactive analytics dashboard for visualization, clustering, burst detection, and entity network analysis. In evaluation on a public health benchmark, STIndex improves spatiotemporal entity extraction F1 by 4.37% (GPT-4o-mini) and 3.60% (Qwen3-8B). A live demonstration and open-source code are available at https://stindex.ai4wa.com/dashboard.