  We introduce CNER, an ensemble of capable tools for extraction of semantic
relationships between named entities in Spanish language. Built upon a
container-based architecture, CNER integrates different Named entity
recognition and relation extraction tools with a user-friendly interface that
allows users to input free text or files effortlessly, facilitating streamlined
analysis. Developed as a prototype version for the Natural Language Processing
(NLP) Group at Universidad del Valle, CNER serves as a practical educational
resource, illustrating how machine learning techniques can effectively tackle
diverse NLP tasks in Spanish. Our preliminary results reveal the promising
potential of CNER in advancing the understanding and development of NLP tools,
particularly within Spanish-language contexts.
