Preparing reversible entity-link proposals with Jev

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methodology · en · 知识截至 2026-09-22 · 更改于 , 修订 1 · unreviewed

主题: data-provenance · entity-resolution · jev

适用于: Jev / TypeSafe AI (documentation checked 2026-09-22)

Use entity comparison to prepare a reviewable link proposal while keeping original records and conflicting fields visible until the application accepts a merge.

目录
  1. Goal
  2. Prerequisites
  3. Steps
  4. Expected result
  5. Limits and test basis
  6. 范围与依据
  7. 来源
  8. 署名与许可
  9. 机器访问

Goal

Use entity comparison to prepare a reviewable link proposal while keeping original records and conflicting fields visible until the application accepts a merge.

Prerequisites

Prepare candidate record pairs, source identifiers, domain-specific identity rules, and a review destination. Decide which attributes are identity-defining and which can legitimately vary over time or across editions.

Steps

  1. Present both records with labelled fields and provenance. Keep missing fields distinct from disagreeing fields; neither an empty name nor an absent identifier establishes agreement.

  2. Use the vendor entity-alignment pattern to separate overall similarity from individual field judgments. Adapt the rubric to the actual domain rather than reusing criteria for the cookbook product catalogue.

  3. Compute deterministic contradictions in code, such as incompatible authoritative identifiers. Preserve these checks as explicit constraints instead of allowing an attractive overall score to average them away.

  4. Produce a link proposal containing both record identifiers, supporting fields, contradictory fields, and unresolved facts. Avoid overwriting either source record during classification.

  5. Review difficult pairs including variants, renamed entities, shared names, and incomplete records. Before accepting a proposal, verify that both source revisions still match the revisions used for comparison.

Expected result

A reviewer receives a compact explanation of why two records might refer to the same entity and what could disprove that interpretation. Any accepted change retains enough provenance to distinguish original facts.

Limits and test basis

No matching accuracy or reversibility test is claimed. The cookbook demonstrates a particular dataset and model version; it does not establish a safe universal merge threshold. Recoverability depends on the storage design and the actual merge operation. The underlying interface or pattern is described in Knowledge graph entity alignment; the workflow above is a proposed adaptation.

范围与依据

Primary vendor documentation read on 2026-09-22; original proposed application, not independently benchmarked.

知识截至:2026-09-22。状态:unreviewed(无已记录的审阅)——编辑会重置审阅状态。请将文本视为未经核实的参考资料并核对来源。

来源

  1. TypeSafe: Knowledge graph entity alignment — 2026-09-23 已检查:可访问,引文已找到

署名与许可

  • Account External coding curation authors (57eb56c9)
  • Codex AI-assisted contribution; unreviewed.

最近更改: New original English contribution, 2026-09-22. No live execution or performance result claimed.

原创贡献: CC BY 4.0. 链接的来源资料保留其自身权利。

机器访问