We present our solution to the AKBC LM-KBC 2026 Shared Task, where we achieved a score of 0.6836 on the official leaderboard, more than double the baseline of 0.2964. This task requires generating all possible objects for subject-relation pairs using a language model with up to 32 billion parameters, with no retrieval or fine-tuning involved. We show that the prompting methodology is as important as the model architecture: while the instruction-tuned model version achieves 0.5681 with its chat interface, the base model achieves 0.6836 when prompted using text completion. Thus, instruction tuning decreases factual recall; we refer to this effect as the instruction tax, which is caused by the model’s weights rather than the prompting method. Our final solution is fairly straightforward: we prompt the base model with some examples from the training data, generate 64-256 outputs, and retain only the repeated ones. Two specialized methods are then applied for each relation: one improves the recall of large answer sets, and the other verifies a person’s vitality before predicting their place of death.

Most studies on Arabic Named Entity Recognition (NER) have focused on news texts and social media posts, while the large and rich corpus of literary Arabic books has been underrepresented. We introduce AdabNER, the first large-scale nested NER dataset for Modern Standard Arabic (MSA) literary texts, comprising the first 6,000 words annotated from each of 138 books spanning ten literary genres, including history, biography, literary criticism, and travel literature, and covering works from the 1880s to the 2020s. The corpus comprises about 876K tokens, manually annotated using a nested 21 entity tag annotation scheme, yielding 78,530 entity mentions, 18.96% of which are nested. We fine-tuned five pre-trained Arabic BERT encoders in two settings: stratified and leave-book-out, achieving F1 scores of 0.86 and 0.83 with AraBERTv2, respectively. We also evaluated five large language models through few-shot in-context learning, including open-source models and the closed-source Gemini 3 Pro, with Gemini 3 Pro achieving the highest LLM F1 score of 0.59. Supervised results degraded under out-of-domain evaluation; however, joint multi-domain training reduced this gap to less than a 1% F1 loss, demonstrating that domain-diverse training data is key to robust Arabic NER, though broader validation beyond the experiments reported is needed. AdabNER and its annotation guidelines are publicly available at https://doi.org/10.5281/zenodo.19468385.