Large language models (LLMs) are increasingly applied to automatic personality assessment, yet most prior work relies on coarse binary labels and direct domain-level predictions, limiting interpretability and ignoring the hierarchical facet structure of personality. In this study, we implement a structured prompting approach with three complementary objectives: direct domain-level prediction, fine-grained facet-level prediction, and domain-level prediction informed by facet outputs. All predictions use a five-level ordinal label scheme, capturing a continuum from very low to very high trait expression. Across all prompt types, we adopt an error-guided self-refinement procedure using in-context learning (ICL) to guide the model toward more accurate predictions. Zero-shot prompts assess baseline performance, while one-shot prompts incorporate a single demonstration example selected through the refinement procedure. Our framework evaluates both domain- and facet-level predictions, enabling examination of how prediction granularity and targeted exemplar selection influence LLM inference. By combining hierarchical domain-facet relationships with structured prompting and refinement, this work aims to provide a systematic approach for interpretable and principled LLM-based personality assessment from long-form life narratives.