Triple
T11653904
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Azure portal |
E276971
|
entity |
| Predicate | supportsService |
P203
|
FINISHED |
| Object | Azure Machine Learning |
E185664
|
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: Azure Machine Learning | Statement: [Azure portal, supportsService, Azure Machine Learning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Azure Machine Learning Context triple: [Azure portal, supportsService, Azure Machine Learning]
-
A.
Azure Machine Learning
chosen
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
-
B.
Oracle Machine Learning
Oracle Machine Learning is a suite of in-database machine learning algorithms and tools from Oracle that enables data scientists and analysts to build, deploy, and manage predictive models directly within Oracle databases.
-
C.
ML.NET
ML.NET is an open-source, cross-platform machine learning framework for .NET developers to build and integrate custom ML models into .NET applications.
-
D.
Azure Cognitive Services
Azure Cognitive Services is a suite of cloud-based AI APIs and tools that enable developers to add capabilities like vision, speech, language understanding, and decision-making to their applications without needing deep machine learning expertise.
-
E.
Amazon SageMaker
Amazon SageMaker is a fully managed cloud service that enables developers and data scientists to build, train, and deploy machine learning models at scale.
- 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_69d6aafbb3c081908a9cdb4ecb8d981d |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a2d01f9c8190849f252f22519550 |
completed | April 10, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee880eccc4819096e2b3305cdb3239 |
completed | April 26, 2026, 9:47 p.m. |
Created at: April 8, 2026, 9:39 p.m.