This dataset was created to train and evaluate the robustness of a 6G Cognitive Coordinator model on the five trustworthiness domains based on the text-based user-intent with the input of five domain experts, each specializing in one of the trust-related classes: Reliability, Privacy, Security, Resilience, and Safety. It consists of annotated phrases and sentences for each class with corresponding trustworthiness scores, which a user could ask for.To enhance the dataset's diversity and robustness, data augmentation techniques were applied. These augmentations aim to simulate real-world variations and improve the model's generalization capabilities:1. Synonym Replacement: A subset of words in the text was replaced with synonyms derived from WordNet to preserve the original context while creating variability.2. Normalization: Scores were normalized to a range of 0 to 1 for consistency and to facilitate regression-based learning.