Triple

T148204
Position Surface form Disambiguated ID Type / Status
Subject Tableau E3373 entity
Predicate integratesWith P1075 FINISHED
Object SQL Server E7155 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: SQL Server | Statement: [Tableau, integratesWith, SQL Server]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SQL Server
Context triple: [Tableau, integratesWith, SQL Server]
  • A. SQL Server chosen
    SQL Server is Microsoft's enterprise-grade relational database management system used for storing, managing, and analyzing data in a wide range of applications.
  • B. SQL
    SQL (Structured Query Language) is a standardized programming language used to manage, query, and manipulate data in relational database management systems.
  • C. Tableau
    Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
  • D. Dynamics 365
    Dynamics 365 is Microsoft’s cloud-based suite of integrated business applications that combines enterprise resource planning (ERP) and customer relationship management (CRM) capabilities.
  • E. Power BI
    Power BI is a Microsoft business analytics and data visualization platform used to transform, analyze, and present data through interactive dashboards and reports.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a257ecb6f48190992c4c8ca908a81c completed Feb. 28, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2c27754a881908ef5a96e05e515e3 completed Feb. 28, 2026, 10:24 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.