Evidence grounding makes generated content verifiable by linking statements to their sources. However, effective verifiability requires two properties that existing methods fail to jointly provide: *verification granularity*, the ability to localize fine-grained evidence for each statement, and *verification completeness*, the ability to capture both support and contradiction. Existing methods either operate at granularities too coarse for rapid inspection or are support-only. We formalize *polarity-aware evidence grounding*: linking each response sentence to its contextual evidence with a supporting, contradicting, or irrelevant label. We construct **eTracer-Bench**, a human-annotated benchmark with 7,905 supporting and contradicting sentence pairs across seven domains, covering both explicit and implicit contradictions. We further propose **eTracer**, a post-hoc framework whose context-aware signed scorer recovers the full evidence map in a single forward pass per response, with localization and polarity emerging jointly from one mechanism. **eTracer** achieves the best overall Citation F1 of **82.39**, driven by a Contradict F1 of **89.20**. A pre-registered within-subjects user study (n = 30) shows that polarity-aware grounding nearly doubles contradiction detection over the document-level baseline (**27.9% → 51.9%**, Cohen’s d = 0.82, p = 0.0003), and suggests a false-completeness bias under which support-only grounding fails to improve over this baseline (**23.4% vs. 27.9%**). Dataset and code: https://github.com/chubohao/eTracer.