The representation of grammatical knowledge in individual LLM neurons and attention heads remains poorly understood. We replicate the feature-based approach of Kryvosheieva et al. (2026) that uses functional localisation to illustrate that distinct syntactic phenomena can recruit the same units. We reproduce and extend the paper to new minimal pair datasets (Dutch, Turkish, and Russian) and additional interpretability techniques: Attention-based analysis, Differential Binary Masking (DBM), and Distributed Alignment Search (DAS). We find reproducible and robust evidence for syntactic localisation in LLMs across several methods and languages. However, localisation strength depends on interpretability techniques (particularly for DBM) and varies across languages, highlighting the value of validating mechanistic claims using several techniques. Code and data available via https://anonymous.4open.science/r/syntax-interpretability-F4C3