Search papers, labs, and topics across Lattice.
The paper introduces the Guardian Parser Pack, an AI pipeline leveraging LLMs to extract and normalize missing-person intelligence from heterogeneous data sources into a unified, schema-compliant format. This system integrates multi-engine PDF text extraction, rule-based source identification, schema-first harmonization, and LLM-assisted extraction with validator-guided repair. Experiments on a manually aligned dataset of 75 cases showed that the LLM-assisted pathway significantly improved extraction quality (F1 = 0.8664) compared to a deterministic approach (F1 = 0.2578), albeit with a substantial increase in processing time.
LLMs can dramatically improve information extraction from messy, real-world investigative documents, but at a steep cost in processing time.
Missing-person and child-safety investigations rely on heterogeneous case documents, including structured forms, bulletin-style posters, and narrative web profiles. Variations in layout, terminology, and data quality impede rapid triage, large-scale analysis, and search-planning workflows. This paper introduces the Guardian Parser Pack, an AI-driven parsing and normalization pipeline that transforms multi-source investigative documents into a unified, schema-compliant representation suitable for operational review and downstream spatial modeling. The proposed system integrates (i) multi-engine PDF text extraction with Optical Character Recognition (OCR) fallback, (ii) rule-based source identification with source-specific parsers, (iii) schema-first harmonization and validation, and (iv) an optional Large Language Model (LLM)-assisted extraction pathway incorporating validator-guided repair and shared geocoding services. We present the system architecture, key implementation decisions, and output design, and evaluate performance using both gold-aligned extraction metrics and corpus-level operational indicators. On a manually aligned subset of 75 cases, the LLM-assisted pathway achieved substantially higher extraction quality than the deterministic comparator (F1 = 0.8664 vs. 0.2578), while across 517 parsed records per pathway it also improved aggregate key-field completeness (96.97\% vs. 93.23\%). The deterministic pathway remained much faster (mean runtime 0.03 s/record vs. 3.95 s/record for the LLM pathway). In the evaluated run, all LLM outputs passed initial schema validation, so validator-guided repair functioned as a built-in safeguard rather than a contributor to the observed gains. These results support controlled use of probabilistic AI within a schema-first, auditable pipeline for high-stakes investigative settings.
