The paper presents the second edition of the WojoodNER shared task, focusing on fine-grained Arabic Named Entity Recognition (NER). The task includes three subtasks: Closed-Track Flat Fine-Grained NER, Closed-Track Nested Fine-Grained NER, and an Open-Track NER for the Israeli War on Gaza. A total of 43 teams registered, with five teams participating in the Flat Fine-Grained Subtask, two teams in the Nested Fine-Grained Subtask, and one team in the Open-Track NER Subtask. The winning teams achieved F1 scores of 91% and 92% in the Flat Fine-Grained and Nested Fine-Grained Subtasks, respectively, while the Open-Track Subtask team achieved an F1 score of 73.7%. The paper details the datasets, evaluation metrics, and submission procedures, and provides an overview of the participating teams and their systems. It highlights the challenges and advancements in Arabic NER, emphasizing the effectiveness of various approaches, particularly those leveraging language models. The authors also discuss the limitations of the dataset and future directions for improving Arabic NER.The paper presents the second edition of the WojoodNER shared task, focusing on fine-grained Arabic Named Entity Recognition (NER). The task includes three subtasks: Closed-Track Flat Fine-Grained NER, Closed-Track Nested Fine-Grained NER, and an Open-Track NER for the Israeli War on Gaza. A total of 43 teams registered, with five teams participating in the Flat Fine-Grained Subtask, two teams in the Nested Fine-Grained Subtask, and one team in the Open-Track NER Subtask. The winning teams achieved F1 scores of 91% and 92% in the Flat Fine-Grained and Nested Fine-Grained Subtasks, respectively, while the Open-Track Subtask team achieved an F1 score of 73.7%. The paper details the datasets, evaluation metrics, and submission procedures, and provides an overview of the participating teams and their systems. It highlights the challenges and advancements in Arabic NER, emphasizing the effectiveness of various approaches, particularly those leveraging language models. The authors also discuss the limitations of the dataset and future directions for improving Arabic NER.