This paper introduces the first Transliteration disambiguation (TD) dataset for Romanized Sinhala, informally known as Singlish, developed to address the challenge of transliteration ambiguity in backwards transliteration tasks. The dataset covers 22 ambiguous Romanized Sinhala words, each mapping to two distinct Sinhala meanings, and provides 30 Romanized sentences per word: ten for each meaning individually and ten containing both meanings in context. Sentences were initially collected through web scraping and later post-processed using the Claude language model, which offers strong support for Sinhala, alongside a rule-based Romanization process to ensure linguistic quality and consistency. To demonstrate its applicability, the dataset was used to evaluate four existing back-transliteration systems, highlighting their performance in resolving context-sensitive ambiguities. Baseline evaluations confirm the dataset’s effectiveness in assessing transliteration systems’ ability to handle transliteration ambiguity, offering a valuable resource for advancing TD and transliteration research for Sinhala.