Named Entity Recognition for dialectal Arabic remains severely constrained by data scarcity, and LLM-based augmentation is the natural remedy. We study LLM-based augmentation for the Lebanese and Palestinian sub-corpora of Wojood 2024, using LLaMA-3.1-8B-Instruct as a generation backbone, and compare three strategies (entity-level, context-level, combined) across two k-shot sampling paradigms. The proposed multi-dimensional pipeline yields a per-sentence composite score Q(s) that exposes a sharp dialectal-authenticity gap between entity- and context-level rewriting (DA 0.92 vs. 0.51). Despite lower register fidelity, context augmentation with curriculum training delivers the most consistent gains (Curras F1 +5.70 pp); entity augmentation lifts Lebanese F1 from a zero baseline to 50.00 on Lebanese-only training, a figure the 85-sentence test set makes coarse. Ablation, weight-sensitivity, threshold sweep, a random-selection control, and a second LLM backbone show that the dialectal-authenticity dimension carries most of Q(s)’s protective effect on Lebanese and that generator choice is itself a first-class register-preservation lever.