This paper presents the Third PASCAL Recognizing Textual Entailment Challenge (RTE-3), detailing the dataset creation methodology and the submissions from 26 participants. The challenge aimed to create a benchmark for recognizing textual entailment, a task crucial for various NLP applications such as Question Answering (QA), Information Extraction (IE), Summarization, Machine Translation, and Paraphrasing. Key innovations in RTE-3 included longer texts to simulate realistic scenarios and a resource pool for sharing tools and resources. A pilot task was also introduced to differentiate unknown entailments from identified contradictions and provide justifications for system decisions. The evaluation process and results are discussed, showing an improvement in system performance compared to previous challenges. The paper concludes by highlighting the progress made in the field and suggesting future directions for research, including theoretical refinements and improved data generation and evaluation methodologies.This paper presents the Third PASCAL Recognizing Textual Entailment Challenge (RTE-3), detailing the dataset creation methodology and the submissions from 26 participants. The challenge aimed to create a benchmark for recognizing textual entailment, a task crucial for various NLP applications such as Question Answering (QA), Information Extraction (IE), Summarization, Machine Translation, and Paraphrasing. Key innovations in RTE-3 included longer texts to simulate realistic scenarios and a resource pool for sharing tools and resources. A pilot task was also introduced to differentiate unknown entailments from identified contradictions and provide justifications for system decisions. The evaluation process and results are discussed, showing an improvement in system performance compared to previous challenges. The paper concludes by highlighting the progress made in the field and suggesting future directions for research, including theoretical refinements and improved data generation and evaluation methodologies.