WiNCC is a reproducible and automatic construction pipeline for building an English–Nepali cross-lingual retrieval corpus that is difficulty-aware, relying on public Wikipedia and Wikidata. Part 1 reproduces a CLIRMatrix-style baseline. Nepali Wikipedia was crawled, articles resolved to a Wikidata QID, then intersected with Wikidata sitelinks to obtain Nepali–English query pairs and the corresponding English document corpus. These were sampled into gold pairs and padded with random English distractors to form a document baseline corpus. Part 2 adds a data-enrichment stage to Part 1 with multi-query, class-matched hard negatives. We compare the produced corpus against existing cross-lingual retrieval resources, run cross-lingual retrieval baselines with three distinct multilingual embedding models (BGE-M3, multilingual-E5-large, and Qwen3-Embedding-4B), and analyze slice-wise quality metadata and query-type-wise field metrics supporting the corpus’s use as a difficulty-aware benchmark. Through our corpus, we also find (1) a 27.8–41.5-point NDCG@10 drop from English to Nepali, (2) descriptions are 5–6× harder than titles, and (3) hard negatives are 2× stickier than random distractors.