---
language:
- en
- de
license: cc-by-4.0
task_categories:
- text-classification
- other
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)