Cross-Border Entity Resolution Gateway with OpenAI Agents SDK
Cross-border entity resolution in global payments suffers from fragmented legal entity databases and inconsistent transliteration standards. Build an entity resolution pipeline using OpenAI Agents SDK to unify global business identifiers, verify legal ownership structures, and achieve 95% F1-score on ambiguous cross-border entity linking tasks.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate an intelligent cross-border legal entity resolution pipeline using the OpenAI Agents SDK and function calling capabilities.
How work is evaluated
Evaluates F1-score of entity matching on multi-jurisdictional corporate dataset with non-standard spellings.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
valid_canonical_id
Ensures all non-null match results map to a valid LEI or canonical ID format.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
entity_matching_f1
F1-score of linked entities vs ground truth annotations. • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct an OpenAI Agents SDK workflow to reconcile messy multi-jurisdictional company profiles
Implement function calling tools for querying corporate registries across disparate regions
Apply fuzzy match, phonetic translation, and LLM semantic alignment for corporate entity names
Achieve 95% F1-score on linking fragmented cross-border corporate payment records
Reference links and supporting material
Synthetic dataset of 600 global payment records featuring ambiguous legal names, transliterated foreign scripts, and missing tax IDs.
Challenge setup
Evaluates F1-score of entity matching on multi-jurisdictional corporate dataset with non-standard spellings.
Challenge input
JSON raw record containing company_name, jurisdiction, tax_id_raw, and address
OpenAI Agents SDK
OpenAI's SDK for autonomous multi-turn tool calling and multi-agent coordination.
Evaluated output
JSON match object containing canonical_entity_id, match_confidence, and reasoning
- Ensures all non-null match results map to a valid LEI or canonical ID format.
- F1-score of linked entities vs ground truth annotations. • target: 0.95 • range: 0-1
- Benchmark: EntityLink-Global-v1
- Entity Matching F1 target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- OpenAI · Required
- Azure OpenAI Service · Optional
- OpenAI o3 · Optional
Evaluation contract
- valid_canonical_id · Weight 1
- entity_matching_f1 · Weight 1
Metrics
- entity_matching_f1 · Target 0.95
Benchmarks
- EntityLink-Global-v1
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON raw record containing company_name, jurisdiction, tax_id_raw, and address
Output: JSON match object containing canonical_entity_id, match_confidence, and reasoning
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.