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Large language models (LLMs) offer significant potential for constructing commonsense knowledge graphs from text, demonstrating adaptability across diverse domains. However, their effectiveness varies significantly with domain-specific language, highlighting a critical need for specialized benchmarks to assess and optimize knowledge graph construction sub-tasks like named entity recognition, relation extraction, and entity linking. Currently, domain-specific benchmarks are scarce. To address this gap, we introduce SynEL, a novel benchmark developed for evaluating text-based knowledge extraction methods, validated using customer support dialogues. We present a comprehensive methodology for benchmark construction, propose two distinct approaches for generating synthetic datasets, and evaluate accumulated hallucinations. Our experiments reveal that existing LLMs experience a significant performance drop, with micro-F1 scores decreasing by up to 25 absolute points when extracting low-resource entities compared to high-resource entities from sources like Wikipedia. Furthermore, by incorporating synthetic datasets into the training process, we achieved an improvement in micro-F1 scores of up to 10 absolute points. We publicly release our benchmark and generation code to demonstrate its utility for fine-tuning and evaluating LLMs.