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Challenge

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.

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Challenge brief

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.

Delivery guide

How work is evaluated

Evaluation

Evaluates F1-score of entity matching on multi-jurisdictional corporate dataset with non-standard spellings.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

valid_canonical_id

Ensures all non-null match results map to a valid LEI or canonical ID format.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

entity_matching_f1

F1-score of linked entities vs ground truth annotations. • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

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

Resources and assets

Reference links and supporting material

Dataset notes

Synthetic dataset of 600 global payment records featuring ambiguous legal names, transliterated foreign scripts, and missing tax IDs.

How this agent runs

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

Checks for
  • 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
Proof of success
  • Benchmark: EntityLink-Global-v1
  • Entity Matching F1 target: 0.95
  • 1 public reference case
Runtime evidence
  • Python execution harness
  • Python sandbox
View technical recipe

Configured tools

Action Space
  • OpenAI · Required
  • Azure OpenAI Service · Optional
  • OpenAI o3 · Optional

Evaluation contract

  • valid_canonical_id · Weight 1
  • entity_matching_f1 · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

Frequently Asked Questions about Cross-Border Entity Resolution Gateway with OpenAI Agents SDK