Triple

T272129
Position Surface form Disambiguated ID Type / Status
Subject Google Workspace E5654 entity
Predicate integration P1075 FINISHED
Object Salesforce E17666 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: Salesforce | Statement: [Google Workspace, integration, Salesforce]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Salesforce
Context triple: [Google Workspace, integration, Salesforce]
  • A. Salesforce chosen
    Salesforce is a leading cloud-based customer relationship management (CRM) company known for its suite of enterprise applications for sales, service, marketing, and analytics.
  • B. NetSuite
    NetSuite is a cloud-based enterprise resource planning (ERP) and business management software suite widely used by companies to manage finance, operations, and customer relationships.
  • C. Siebel Systems
    Siebel Systems was a leading enterprise software company best known for pioneering customer relationship management (CRM) solutions for large organizations.
  • D. Marketo
    Marketo is a leading marketing automation software platform that helps businesses manage and optimize digital marketing campaigns and customer engagement.
  • E. Siebel
    Siebel is a surname most prominently associated with Jennifer Siebel Newsom, an American documentary filmmaker and the First Partner of California.
  • 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_69a25853594c8190b05ec3a586ec88bf completed Feb. 28, 2026, 2:52 a.m.
NER Named-entity recognition batch_69a260d0dae48190a2ec98d0186fd792 completed Feb. 28, 2026, 3:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69a38f537f1c8190a59ac4669498a3fc completed March 1, 2026, 12:58 a.m.
Created at: Feb. 28, 2026, 2:57 a.m.