Driven by Structural Empty Graph Networks (SEGN)——A Methodological Analysis of Latvia's SAB Reporting Mechanism Based on the "Single Source of Truth – Computable Reasoning – Audit Interface" Framework

This dataset takes the 2025 Annual National Security Report published by the Constitutional Protection Bureau (Satversmes aizsardzības birojs, SAB) of the Republic of Latvia as its research subject. It represents the first systematic conversion of a national security public report into a computable, auditable, and transferable semantic reasoning data product. The entire data construction process is grounded in the Structural Empty Graph Network (SEGN) methodology, designed to address the core limitations of traditional text analysis in simultaneously meeting the requirements of evidence traceability, reasoning verifiability, and conclusion comparability. Unlike common thematic models, keyword co-occurrence analyses, or abstract-based datasets, this collection does not aim to summarise textual content. Instead, it deconstructs factual assertions, governance mechanisms, causal relationships, and evidence anchors within the report into structured entities. These are then transformed into operational reasoning units through rigorous threshold rules. The dataset comprises three highly coupled yet independently usable layers: First, the Single Source of Truth (SSOT) layer, which precisely maps each semantic object to its page and paragraph within the original report via a lossless knowledge graph, ensuring any conclusion can be traced back to specific textual evidence; Second, the Computable Reasoning Layer distinguishes between ‘concept-mechanism-causality’ reasoning types, introducing evidence alignment thresholds and coverage thresholds to eliminate reliance on researcher subjectivity in determining reasoning strength; Third, the Audit Interface Layer records evidence sufficiency, uncertainty status, and rebuttable conditions for inferred objects within structured fields, facilitating third-party verification and cross-year comparisons. This dataset's unique value lies in its avoidance of generating additional intelligence assessments or predictive conclusions. Instead, it transforms national security texts into a repeatable, threshold-upgradable, falsifiable collection of semantic and inferential objects. This enables researchers to address questions challenging for traditional methods, such as: which conclusions are ‘audit-passable’ in terms of evidence versus those remaining at ‘early warning level’; which mechanisms within reports constitute critical nodes genuinely underpinning governance decisions; and whether deliberately retained textual uncertainty itself constitutes an institutional signal. This dataset serves multiple research contexts including national security studies, policy text analysis, intelligence methodology research, explainable artificial intelligence, AI risk governance, and evidence-based decision support. It also functions as a standardised template for semantic auditing and comparative analysis of annual reports from other nations or organisations. Through the SEGN methodology, this dataset provides a reusable data foundation for transforming ‘readable reports’ into ‘auditable objects of reasoning’.