Multicultural Singapore hosts overlapping language publics (English, Chinese, and Malay) that discuss the same out-groups in parallel, a natural setting to ask whether online hate shares a structure across languages and whether what a community $\textit{produces}$ is what it $\textit{amplifies}$. From a Singapore-centric 2025 Facebook, Reddit, and YouTube corpus (31.0M items; 1.76M comments mentioning eleven identity groups), we benchmark eight open large language models as hate annotators against a human-adjudicated gold set, adopt the best (Phi-4: accuracy 0.95, Cohen's $κ$=0.91, recall 1.00 on an independent manual check), and replicate every finding under a second model. The results converge on one thesis, $\textit{layered cultural contingency}$: cross-lingual divergence falls monotonically as one moves from what a community hates to how and why it hates. Which out-groups are targeted is culturally specific (language $\times$ target $V$=0.25), but the threat frames and the binding moral grammar of hate (sanctity and loyalty, $55-75\%$, not fairness) are far more shared across languages, with divergence dropping to $V$=0.08 for moral foundations and 0.07 for emotion. Hate is contempt-driven and voices an out-group, anti-immigration grievance rather than an anti-system one. Reception is selectively nativist: hateful comments are amplified less than neutral mentions overall, yet anti-immigrant hate is preferentially amplified while religious and anti-LGBTQ hate is not, and volume does not track 2025 Singapore key events. We further show that absolute hate prevalence is not well defined at the LLM-annotator level, with agreement ceilings at $κ\approx0.42$ across models, so we report relative structure as primary. The findings bear directly on cross-lingual content moderation.

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