Developing large-scale industrial systems requires high-quality requirements to avoid costly rework and project delays. However, linguistic ambiguities in natural language (NL) requirements have been a long-standing challenge, often introducing misinterpretations and inconsistencies that propagate throughout the development lifecycle. Such ambiguous NL requirements necessitate early detection and well-reasoned explanations to clarify and prevent further misunderstandings among stakeholders. While solutions have been developed to detect ambiguities in NL requirements, the advent of generative large language models (LLMs) offers new avenues for explanation-augmented requirements ambiguity detection. This paper empirically investigates LLMs for ambiguity detection and explanation in real-world industrial requirements by adopting an in-context learning paradigm. Our results from three industrial datasets show that LLMs achieve a 20.2% average performance increase in classifying ambiguous requirements when prompted with ten relevant in-context demonstrations (10-shot), compared to no demonstrations (0-shot). Additionally, we conducted human evaluations of the LLM-generated outputs with eight industry experts along four dimensions—naturalness, adequacy, usefulness and relevance—to gain practical insights. The results show an average rating of 3.84 out of 5 across evaluation criteria, indicating that the approach is effective in providing supporting explanations for requirement ambiguities.