CrossER: A Benchmark for Context-Dependent Cross-System Entity Resolution

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

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

[
  {"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:

git clone https://github.com/nihalgunu/CrossER
pip install -r requirements.txt
python -m generate.generate_all --seed 42

Citation

@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

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Phyvant · GitHub · Paper (NeurIPS 2026)