For intervention in information disorder on social media, detection alone is insufficient because the volume and speed of circulation make full investigation of all disorder infeasible. This paper develops a conceptual prioritisation framework for information disorder on X (formerly Twitter), using Community Notes as an intervention-focused frontier sampling mechanism to narrative level instead of individual post level. A linguistic risk score measuring volatility of language to proxy for veracity, and a belief proxy score to proxy for vulnerability of users towards believing disordered information were used to define a 9-state risk-belief space, with state transition modelling using a Markov chain under partial observability. The results were used to construct two indices - Credibility Influence Index (CII) for directional drift in belief and the Attention Displacement Index (ADI) for magnitude of linguistic volatily. Monte Carlo bootstrap resampling provides uncertainty estimates. Empirical analysis on six narrative clusters supports a three-way prioritise/observe/deprioritise decision logic, offering a reproducible, uncertainty-aware conceptual triage framework, establishing the feasibility of a robust intervention prioritisation framework.