<p>While Large Language Models (LLMs) excel at generating fluent text, they frequently fail in low-resource, morphologically complex settings (such as Uzbek, Kazakh, Kyrgyz). In these scenarios, models often prioritize surface-level fluency over structural fidelity, leading to plausible-sounding but factually incorrect hallucinations. To address this, we introduce the Structure-Guided Generation and Verification (SGV) framework, a two-stage approach designed to construct high-quality parallel corpora. First, we use Interlinear Glossed Text (IGT) as a structural constraint, guiding the LLM to follow the grammatical rules of the source language. Second, we employ a Dual-Encoder Verification module to detect and filter out translations that deviate from these constraints. Extensive experiments on three Turkic-Chinese translation tasks demonstrate that SGV significantly outperforms competitive baselines, including LASER and LaBSE mining strategies. Crucially, our analysis reveals a Resilience Gap: the framework maintains high performance even when upstream linguistic tools are noisy, effectively isolating final corpus quality from tool imperfections. Furthermore, we find that SGV is model-agnostic, providing substantial gains across different LLM architectures. These findings suggest that incorporating explicit structural guidance is a more effective strategy for low-resource translation than relying solely on scale or implicit learning. The code is available at <a href="https://github.com/almjan/SGV-LowRes-NMT">https://github.com/almjan/SGV-LowRes-NMT</a></p>