Output format is often an unreported factor in LLM evaluations for structured NLP tasks such as Slot Filling or Named Entity Recognition. This work proposes to explore the impact of the output structured format generated by LLMs. We show that measured performance and reliability depend on the requested format (JSON, XML or inline Key-Values). A study is performed across four SLU and three NER benchmarks and considering 13 instruction-tuned open-weight LLMs, using standardized and open-source prompts and parsers. This format-specific evaluation reveals statistically significant swings of 2-46 F1 points depending on model and dataset. Additionally, we propose a lightweight selection procedure to determine the best format per model-dataset combination using only a small development slice; thus reducing trial-and-error in practice.

Knowledge Graph construction from natural language requires extracting structured triplets from complex, information-dense sentences. In this paper, we investigate if the decomposition of text into atomic propositions (minimal, semantically autonomous units of information) can improve the triplet extraction. We introduce MPropositionneur-V2, a small multilingual model covering six European languages trained by knowledge distillation from Qwen3-32B into a Qwen3-0.6B architecture, and we evaluate its integration into two extraction paradigms: entity-centric (GLiREL) and generative (Qwen3). Experiments on SMiLER, FewRel, DocRED and CaRB show that atomic propositions benefit weaker extractors (GLiREL, CoreNLP, 0.6B models), improving relation recall and, in the multilingual setting, overall accuracy. For stronger LLMs, a fallback combination strategy recovers entity recall losses while preserving the gains in relation extraction. These results show that atomic propositions are an interpretable intermediate data structure that complements extractors without replacing them.