Triple
T277336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Power BI |
E5276
|
entity |
| Predicate | integratesWith |
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: [Power BI, integratesWith, Salesforce]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Salesforce Context triple: [Power BI, integratesWith, 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_69a257e6c8788190987dfe705ca2912a |
completed | Feb. 28, 2026, 2:50 a.m. |
| NER | Named-entity recognition | batch_69a25ded68c88190b1fc595ce329aeb9 |
completed | Feb. 28, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a39153528c8190a0f1e69a3f94b305 |
completed | March 1, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 2:59 a.m.