Corporate sustainability reports include a content index mapping each disclosure required by a reporting framework (e.g. GRI) to its supporting pages — a resource that potentially benefits nearly every downstream sustainability-NLP task, yet no existing benchmark scores its recovery as a framework-grounded, page-resolved output. We formalize Content Index Detection (CID) as three evaluable stages — index page retrieval (S1), table extraction and disclosure linking (S2), and page-label-to-index resolution (S3) — and release esg_cid_plus: 37 GRI reports and 7,294 rows stratified across three difficulty tiers, from unchanged real reports to an adversarial tier of naturally hard documents and synthetic perturbations. Our main benchmark sweeps open-weight vision-language models from sub-1B to 35B parameters (plus frontier-API references) as S2 extractors under shared, deterministic S1 and S3 stages: the best backends reach S2-F1 of 0.89–0.92 on the easier tiers, while standard OCR-benchmark scores explain only a third of the variance in extraction quality. Composing extraction with the deterministic resolver exposes S3 as the open frontier: under label-scheme resolution alone, where even best extraction caps end-to-end F1 at 0.11 on the hardest tier. esg_cid_plus will be released in huggingface.