Document-level Event Extraction (DEE) aims to extract event records from given documents. Previous works typically process documents by constructing complex feature fusion matrices or graph structures, aiming to predict complete event records via entity selection or role filling. However, this holistic structure extraction paradigm often suffers from cascaded error propagation due to rigid entity dependency, multi-role structural omissions limited by selection mechanisms, and consequent scalability bottlenecks. In this paper, we propose a Deconstruction-Reconstruction Framework for Document-Level Event Extraction (DrDEE). This framework deconstructs the holistic event record extraction task into atomic Token-Event-Role (TER) triplet verification, and subsequently reconstructs event records from valid tokens. It realizes a paradigm shift in DEE from Many-to-Many Selection to One-to-One Verification, significantly simplifying the task formulation. Experimental results show that the proposed method outperforms state-of-the-art methods, confirming its effectiveness. Our code is available at https://anonymous.4open.science/r/DrDEE.

Node Importance Estimation (NIE) in Knowledge Graphs (KGs) aims to quantify the significance of entities, serving as a pivotal instrument for deciphering the latent mechanisms of social dynamics. However, existing methods are often confined to supervised paradigms and rely heavily on topological aggregation, resulting in limited generalization capability. To address these challenges, we propose GenNIE, the first end-to-end generative reasoning framework for NIE. Specifically, GenNIE leverages Large Language Models (LLMs) integrated with topological information to generate precise importance scores for entities in KGs. Furthermore, GenNIE introduces a Global-Structural Graph Perception mechanism to empower the LLMs with holistic graph cognition. Extensive experiments demonstrate the performance superiority of GenNIE and its robust generalization across diverse domains. Our code is available at https://github.com/CoffeyF/GenNIE.git.