In this work, we advocate for a collaborative methodology that bridges human expertise with the capabilities of large language models (LLMs). We introduce a novel semiautomated text annotation process that harnesses the power of Google's Gemini Pro language model through structured prompts, ensuring the efficient handling of this annotation task. Although our initial emphasis lay in annotating data for JSON extraction from plain text, it is crucial to acknowledge the occasional challenge posed by the token size limitations of LLMs, particularly when dealing with lengthy input texts. To address this limitation effectively, we expanded our annotation efforts to encompass an abstractive summarization task. This strategic augmentation provides a flexible solution to mitigate the mentioned constraint, also demonstrating the adaptability and versatility of our collaborative approach.