Several studies indicate that the level of predicate-argument structure is relevant for modeling prevalent phenomena in current textual entailment corpora. Although large resources like FrameNet have recently become available, attempts to integrate this type of information into a system for textual entailment did not confirm the expected gain in performance. The reasons for this are not fully obvious; candidates include FrameNets restricted coverage, limitations of semantic parsers, or insufficient modeling of FrameNet information. To enable further insight on this issue, in this paper we present FATE (FrameNet-Annotated Textual Entailment), a manually crafted, fully reliable frame-annotated RTE corpus. The annotation has been carried out over the 800 pairs of the RTE-2 test set. This dataset offers a safe basis for RTE systems to experiment, and enables researchers to develop clearer ideas on how to effectively integrate frame knowledge in semantic inferenence tasks like recognizing textual entailment. We describe and present statistics over the adopted annotation, which introduces a new schema based on full-text annotation of so called relevant frame evoking elements.