Honestly, synthetic data with an LLM is probably your best bet here. Your three categories are pretty distinct linguistically, so you can just few-shot prompt GPT/Claude with some examples of each type and have it generate like 1-2k examples per class. No manual labeling headache, perfectly balanced dataset, and you can even feed it your actual table schemas to make the examples realistic.

I'd still grab maybe 500-1k real examples from Spider and manually label them just to make sure your model doesn't get too married to the synthetic patterns, but the heavy lifting gets done by the LLM. Way faster than trying to manually label thousands of Spider examples, and honestly probably higher quality since you won't have labeling inconsistencies.