Misinformation rarely presents itself as entirely true or entirely false. Instead, it often embeds partial truths within misleading contexts, creating narratives that blur the boundary between fact and falsehood. Traditional binary fact-checking frameworks fail to capture this nuance, forcing complex claims into oversimplified categories. To address this gap, we introduce MEGA, a multidimensional graph framework designed to classify ambiguous claims, with a particular focus on those labelled Somewhat True. MEGA integrates event evidence, spatio-temporal metadata, and a quantifiable nuance score. Its Event Candidate Extraction (ECE) module identifies supporting or contradicting evidence, while the Nuance Control Module (NCM) injects or removes nuance to assess its effect on classification. Experiments show that nuance is both detectable and learnable: adding nuance improves borderline discrimination, while stripping it leads the decisions toward false extremes and conceals partial truth. Our top model— nuance-injected without score weighting— improve accuracy and F1 score by 15 and 16 points over the claims-only baseline, and 6 and 9 points over the ECE-only variant. These results show that explicitly modeling nuance alongside context is crucial for classifying mixed-truth claims and advancing fact-checking beyond binary judgments.