Grounding Large Language Models (LLMs) in chemical knowledge graphs (KGs) offers a promising way to support synthesis planning, but reliably retrieving information from these complex structures remains a challenge. Therefore, this work addresses that gap by constructing a bipartite KG and evaluating Text2Cypher query generation across both single- and multi-step retrieval tasks. Different prompting strategies were tested, including zero-shot, one-shot with static, random, or embedding-based example selection, and a checklist-driven self-correction pipeline. Results indicate that one-shot prompting is most effective when the exemplar aligns with the query both structurally and logically. When such an exemplar is provided as context to the Cypher generation prompt, self-correction does not yield significant performance gains. Overall, this study introduces a reproducible setup for Text2Cypher experimentation and evaluation.