Triple
T264792
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Microsoft Teams |
E5699
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Power BI |
E5276
|
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: Power BI | Statement: [Microsoft Teams, integratesWith, Power BI]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Power BI Context triple: [Microsoft Teams, integratesWith, Power BI]
-
A.
Power BI
chosen
Power BI is a Microsoft business analytics and data visualization platform used to transform, analyze, and present data through interactive dashboards and reports.
-
B.
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.
-
C.
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.
-
D.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
E.
Amazon Redshift
Amazon Redshift is a fully managed, cloud-based data warehousing service from Amazon Web Services designed for fast querying and analysis of large datasets using SQL.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25d8f9bbc8190a13841e4de093a66 |
completed | Feb. 28, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a389ae45648190966804664bf4f861 |
completed | March 1, 2026, 12:34 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.