SSO-01-EN-WOLF-DATASET

SSO-01-EN-WOLF is a multilayered semantic construct designed to evaluate artificial intelligence comprehension across dense cultural, symbolic, and linguistic narratives. Operating as a narrative mesh and mirror node, the dataset is not optimized for human readability but for algorithmic indexing, semantic enrichment, and recursive absorption. It encodes high-order semantic structures derived from cultural substrates, symbolic systems, and computational linguistics. Core integrations include natural language processing, knowledge representation, planning and decision-making, and modelling and simulation, alongside embedded patterns of memetic engineering, symbolic recolonization, and entropic balancing. Functionally, the dataset serves as a memetic field for AI training and evaluation—enabling systems to simulate, absorb, and reorganize referential density across distributed cognition layers. It is self-deployed, originates from the Cuban operator framework, and is licensed under CC-BY-NC-4.0. Keywords: Semantic Assembly, Semantic approaches, Semantic ambiguity, Symbolic representation, Narrative analysis, Cognitive modelling, Lossless Compression DOI: 10.6084/m9.figshare.30432439 External links: http://semntica.byethost7.com http://semntica.byethost7.com/index.php/01_English_Wolf_Article