This work addresses the temporal ordering task of clinical frames in the Basic Life Support (BLS) subset of ClinSkillQA. A two-stage hybrid pipeline based on Qwen2-VL-2B-Instruct in a zero-shot configuration is proposed. In Stage 1, each image is processed independently to extract factual visual evidence, which is then transformed, using deterministic rules, into a structured representation. In Stage 2, ordering is formulated as an ordinal scoring task over procedural stages, with ties broken using PCA applied to multimodal embeddings. Evaluation followed the official benchmark protocol, considering Task Accuracy, Pairwise Accuracy, and BERTScore. In the test phase, the system achieved Task Accuracy = 0.17, Pairwise Micro Accuracy = 0.60, and BERT F1 = 0.71, with complete coverage in both predictions and rationales. The results demonstrate an interpretable and reproducible foundation, although challenges in fine-grained temporal discrimination remain.

Ensuring food safety requires effective detection of potential hazards in food products. This paper presents the participation of VerbaNexAI in the SemEval-2025 Task 9 challenge, which focuses on the automatic identification and classification of food hazards from descriptive texts. Our approach employs a machine learning-based strategy, leveraging a Random Forest classifier combined with TF-IDF vectorization and character n-grams (n=2-5) to enhance linguistic pattern recognition. The system achieved competitive performance in hazard and product classification tasks, obtaining notable macro and micro F1 scores. However, we identified challenges such as handling underrepresented categories and improving generalization in multilingual contexts. Our findings highlight the need to refine preprocessing techniques and model architectures to enhance food hazard detection. We made the source code publicly available to encourage reproducibility and collaboration in future research.