Triple
T4279750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BI Engine |
E97119
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Tableau |
E3373
|
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: Tableau | Statement: [BI Engine, integratesWith, Tableau]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tableau Context triple: [BI Engine, integratesWith, Tableau]
-
A.
Tableau
chosen
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.
-
B.
Looker
Looker is a modern business intelligence and data analytics platform that enables organizations to explore, visualize, and share insights from their data.
-
C.
Tabularium
The Tabularium was the official records office of ancient Rome, a monumental state archive building overlooking the Roman Forum.
-
D.
Aqua Data Studio
Aqua Data Studio is a database management and development environment that provides tools for querying, visualizing, and administering a wide range of relational and NoSQL databases.
-
E.
Tableau Conference
Tableau Conference is an annual analytics and data visualization event hosted by Tableau that brings together users, developers, and data professionals for keynotes, training, and community networking.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350367da48190b735deef9b5d2d2e |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5c7237b608190ab5aca56027344c4 |
completed | March 14, 2026, 8:37 p.m. |
Created at: March 12, 2026, 11:07 p.m.