Large-scale annotated datasets underpin progress in natural language understanding, yet human annotation remains a persistent bottleneck. Large Language Models promise to bridge this gap, but their reliability as annotators is poorly understood. We evaluate 14 open-weight instruction-tuned models and 5 matched base–instruct pairs across 51,189 instances spanning 10 heterogeneous datasets, revealing that annotation failures are structurally predictable rather than random. We identify three recurring failure modes: Safety Hallucination, Intensity Collapse, and Entailment Bias. Safety hallucination can precede alignment rather than being introduced by it: some base models flag up to 100% of benign text as hate speech. Alignment benefits scale inversely with base model capability, yielding a +21.6pp gain for the weakest base model yet a negligible +0.5pp shift for the strongest. KL divergence between base and instruct distributions is 2.0x higher on incorrect predictions, providing a lightweight instance-level signal for alignment-associated errors. Inter-prompt unanimity achieves 72–94% accuracy at 71–90% coverage on clean tasks, yet can actively degrade performance on tasks dominated by shared systematic bias. We translate these findings into a practical deployment framework with pre-screening diagnostics and tiered quality-routing policies, establishing principled boundaries for when LLM annotation can be trusted and when human intervention remains necessary.