The majority of historical experimental data across the empirical sciences remains locked in unstructured legacy formats, which slows data-driven discovery. We introduce an automated multi-modal pipeline that processes large literature corpora, filters non-empirical noise, and outputs structured datasets. Layout-aware document parsing is paired with a lightweight semantic triage gatekeeper to substantially reduce the computational overhead of applying frontier multi-modal models at scale. A generative model reconstructs experimental trajectories from text and figures, while a schema-constrained prompt and a strictly typed programmatic validation layer check physical bounds before serialization. We evaluate the pipeline on a 60-document corpus of extreme-condition thermodynamic literature drawn from Low Energy Nuclear Reactions (LENR) research, reporting concrete throughput, triage, and constraint-violation statistics. We distinguish safeguards enforced in code (physical-bound validators) from those requested through the prompt, such as source grounding, and outline the evaluation needed to verify the latter. The architecture is domain-agnostic by design, and we outline how it could transfer to catalysis, energy storage, and preclinical biomedical research.