The extraction of emissions-related information from annual reports has become increasingly important due to the Corporate Sustainability Reporting Directive (CSRD), which mandates greater transparency in sustainability reporting. As a result, information extraction (IE) methods must be robust, ensuring accurate retrieval while minimizing false values. While large language models (LLMs) offer potential for this task, their black-box nature and lack of specialization in table structures limit their robustness – an essential requirement in risk-averse domains. In this work, we present a two-step hybrid approach which optimizes both accuracy and robustness. More precisely, we combine a rule-based step for table IE with a regularized LLM-based step, both leveraging temporal prior knowledge. Our tests demonstrate the advantages of combining structured rules with LLMs. Furthermore, the modular design of our method allows for flexible adaptation to various IE tasks, making it a practical solution for industry applications while also serving as a scalable assistive tool for information extraction.