This paper presents our systems for the Daleel 2026 Shared Task. We explore multitask learning, domain-specific ensembles, and a two-stage BIO segmentation and classification pipeline with LLM-based disambiguation, alongside classical machine-learning, Transformer, and LLM baselines. Results show that supervised Transformers outperform standalone LLM prompting, while LLMs are more effective for selectively resolving ambiguous predictions. The multitask model generalizes best on Task 1, achieving 0.599 F1, while the ensemble multitask model reaches 0.683 F1 on Task 2. Our results show the potential of LLM integration within Transformer-based systems for Arabic argumentative discourse mining.