Triple

T148201
Position Surface form Disambiguated ID Type / Status
Subject Tableau E3373 entity
Predicate product P490 FINISHED
Object Tableau Mobile 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 Mobile | Statement: [Tableau, product, Tableau Mobile]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Tableau Mobile
Context triple: [Tableau, product, Tableau Mobile]
  • 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. Power BI
    Power BI is a Microsoft business analytics and data visualization platform used to transform, analyze, and present data through interactive dashboards and reports.
  • C. Plotly
    Plotly is an interactive, open-source graphing and data visualization library widely used in Python for creating rich, web-based charts and dashboards.
  • D. TeamViewer
    TeamViewer is a German software company best known for its remote access and remote control solutions that allow users to connect to and manage devices over the internet.
  • E. BARD Mobile app
    The BARD Mobile app is a specialized application that provides accessible audio and braille books and magazines to people who are blind, visually impaired, or print disabled.
  • 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_69a252868de4819080e21c9938bfe8b6 completed Feb. 28, 2026, 2:27 a.m.
NER Named-entity recognition batch_69a257ecb6f48190992c4c8ca908a81c completed Feb. 28, 2026, 2:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2d73164e48190b993fe55aaba30be completed Feb. 28, 2026, 11:53 a.m.
Created at: Feb. 28, 2026, 2:31 a.m.