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README.md

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+---
+language:
+- en
+- de
+license: cc-by-4.0
+task_categories:
+- text-classification
+- text-matching
+tags:
+- entity-resolution
+- record-linkage
+- cross-system-matching
+- enterprise-data
+- benchmark
+- rag
+- information-extraction
+- multilingual
+- neurips-2026
+pretty_name: CrossER
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+    - split: train
+      path: data/splits/train.json
+    - split: validation
+      path: data/splits/val.json
+    - split: test
+      path: data/splits/test.json
+---
+
+# CrossER: A Benchmark for Context-Dependent Cross-System Entity Resolution
+
+[![License: CC BY 4.0](https://img.shields.io/badge/Data-CC%20BY%204.0-green.svg)](https://creativecommons.org/licenses/by/4.0/)
+[![NeurIPS 2026 E&D](https://img.shields.io/badge/NeurIPS%202026-E%26D%20Track-red.svg)](https://neurips.cc/Conferences/2026/CallForEvaluationsDatasets)
+
+**CrossER** is a benchmark for context-dependent cross-system entity resolution where surface features are deliberately misleading. Match pairs average only **0.29 string similarity** (names look unrelated), while non-match pairs average **0.94 similarity** (names look identical).
+
+In real enterprises, matching `Product 4418` to `Maltodextrin DE20 Grade A` requires consulting migration runbooks, classification guides, and Slack threads — not string similarity. CrossER measures the "context gap" across three evaluation modes.
+
+## Dataset Summary
+
+| Metric | Value |
+|--------|-------|
+| Total Entities | 688 |
+| Total Pairs | 1,800 |
+| Match / No-Match / Ambiguous | 800 / 800 / 200 |
+| Source Systems | 5 |
+| Entity Types | 4 |
+| Languages | English, German |
+| Signal Documents | 8 |
+| Noise Documents | 110 |
+| Oracle Context Records | 875 |
+
+## Headline Results
+
+| Method | CrossER-Easy | CrossER-Full | CrossER-Hard |
+|--------|-------------|-------------|-------------|
+| String Matching | 0.741 | 0.363 | 0.000 |
+| Fuzzy Matching | 0.771 | 0.455 | 0.000 |
+| Embedding Matching | 0.964 | 0.559 | 0.000 |
+| Attribute Matching | **1.000** | 0.729 | 0.000 |
+| SBERT (multilingual) | 0.843 | 0.604 | 0.222 |
+| LLM Zero-Shot | -- | 0.090 | 0.000 |
+| LLM + RAG (BM25) | 0.848 | 0.632 | 0.200 |
+| LLM + Oracle | **1.000** | **1.000** | **1.000** |
+
+No-context methods score **0.00 F1** on hard pairs. Oracle context closes the gap completely. RAG partially bridges it — retrieval quality is the bottleneck.
+
+## Evaluation Modes
+
+| Mode | Description |
+|------|-------------|
+| **No Context** | Entity pairs only — what's possible from attributes alone |
+| **Raw Context** | 118 enterprise documents (8 signal + 110 noise) — realistic RAG |
+| **Oracle Context** | 875 structured migration records — upper bound |
+
+## Named Subsets
+
+| Subset | Pairs | Description |
+|--------|-------|-------------|
+| **CrossER-Easy** | 257 | Easy matches + obvious negatives; F1 ceiling = 1.000 |
+| **CrossER-Medium** | 262 | Medium-difficulty pairs; F1 ceiling = 0.776 |
+| **CrossER-Hard** | 203 | Hard matches + adversarial negatives + ambiguous; F1 ceiling = 0.000 (no-context) |
+| **CrossER-Full** | 722 | All test pairs |
+
+## Source Systems
+
+| System | Role | Naming Style |
+|--------|------|-------------|
+| SAP_TC2 | Primary ERP (NA HQ) | Formal English |
+| SAP_CFIN | Financial consolidation | Internal codes / abbreviations |
+| SAP_APAC | APAC regional ERP | Abbreviated with region prefix |
+| LEGACY_ERP | Decommissioned (2019) | Cryptic category codes |
+| SHAREPOINT | Tax/compliance reference | Authoritative long names |
+
+## Dataset Structure
+
+```
+data/
+├── entities.json              # 688 entities across 5 systems
+├── pairs.json                 # 1,800 pairs with difficulty tiers
+├── splits/                    # train (40%) / val (20%) / test (40%)
+├── subsets/                   # CrossER-Easy, -Medium, -Hard, -Full
+└── context/
+    ├── raw/documents/         # 8 signal documents
+    ├── raw/noise/             # 110 noise documents
+    └── structured/            # oracle_context.json (875 records)
+```
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load train/val/test splits
+ds = load_dataset("smurthy5/CrossER")
+
+# Load a named subset
+import json, requests
+easy = json.loads(requests.get(
+    "https://huggingface.co/datasets/smurthy5/CrossER/resolve/main/data/subsets/crosser_easy.json"
+).text)
+```
+
+## Prediction Format
+
+```json
+[
+  {"pair_id": "pair_0001", "predicted_label": "match"},
+  {"pair_id": "pair_0002", "predicted_label": "no_match"}
+]
+```
+
+Valid labels: `match`, `no_match`, `ambiguous`.
+
+## Reproducibility
+
+The dataset is fully reproducible:
+
+```bash
+git clone https://github.com/nihalgunu/CrossER
+pip install -r requirements.txt
+python -m generate.generate_all --seed 42
+```
+
+## Citation
+
+```bibtex
+@inproceedings{crosser2026,
+  author    = {Gunukula, Nihal and Murthy, Sameer},
+  title     = {{CrossER: A Benchmark for Context-Dependent Cross-System Entity Resolution}},
+  booktitle = {NeurIPS 2026 Evaluations \& Datasets Track},
+  year      = {2026},
+  url       = {https://huggingface.co/datasets/smurthy5/CrossER}
+}
+```
+
+## License
+
+- **Code**: Apache 2.0
+- **Data**: CC BY 4.0
+
+---
+
+[Phyvant](https://phyvant.com) · [GitHub](https://github.com/nihalgunu/CrossER) · [Paper (NeurIPS 2026)](https://neurips.cc/Conferences/2026/CallForEvaluationsDatasets)