Triple

T2097902
Position Surface form Disambiguated ID Type / Status
Subject Power BI Gateway E37023 entity
Predicate supportsDataSource P24486 FINISHED
Object SAP HANA E35622 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: SAP HANA | Statement: [Power BI Gateway, supportsDataSource, SAP HANA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SAP HANA
Context triple: [Power BI Gateway, supportsDataSource, SAP HANA]
  • A. SAP
    SAP is the commonly used abbreviation for the Société d’Anthropologie de Paris, a French learned society dedicated to the study of anthropology.
  • B. SAP chosen
    SAP is a leading global enterprise software company best known for its ERP solutions that help organizations manage business operations and customer relations.
  • C. SAP
    SAP was the former official currency of South Africa, used before the adoption of the South African rand.
  • D. IBM DB2
    IBM DB2 is a family of enterprise-grade relational database management systems developed by IBM, widely used for high-performance, scalable data storage and transaction processing across mainframe, distributed, and cloud environments.
  • E. 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.
  • 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_69a8861828948190924aa30c08806b3a completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abba9de75c81909770409b5ae62c24 completed March 7, 2026, 5:41 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae3060b834819091dff510b89b3ec8 completed March 9, 2026, 2:28 a.m.
Created at: March 4, 2026, 7:43 p.m.