Triple
T19433992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oracle Object Storage |
E486185
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Oracle Cloud Infrastructure Data Science |
—
|
NE NERFINISHED |
How this triple was built (3 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: Oracle Cloud Infrastructure Data Science | Statement: [Oracle Object Storage, integratesWith, Oracle Cloud Infrastructure Data Science]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Oracle Cloud Infrastructure Data Science Context triple: [Oracle Object Storage, integratesWith, Oracle Cloud Infrastructure Data Science]
-
A.
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.
-
B.
Oracle AI Services
Oracle AI Services is a suite of cloud-based artificial intelligence tools and APIs from Oracle that enable developers to easily add capabilities like machine learning, computer vision, language processing, and anomaly detection to their applications.
-
C.
Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
-
D.
Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Oracle Cloud Infrastructure Data Science Target entity description: Oracle Cloud Infrastructure Data Science is a managed cloud platform for building, training, deploying, and managing machine learning models at scale within the Oracle Cloud ecosystem.
-
A.
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.
-
B.
Oracle AI Services
Oracle AI Services is a suite of cloud-based artificial intelligence tools and APIs from Oracle that enable developers to easily add capabilities like machine learning, computer vision, language processing, and anomaly detection to their applications.
-
C.
Azure Machine Learning
Azure Machine Learning is a cloud-based service from Microsoft for building, training, deploying, and managing machine learning models at scale on Azure.
-
D.
Vertex AI
Vertex AI is Google Cloud’s unified machine learning platform for building, training, and deploying ML models at scale.
-
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. chosen
Provenance (2 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_69d8e8d7ad488190a3373045029b0f3b |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6335dae10819096e1825741f814ed |
completed | April 20, 2026, 2:08 p.m. |
Created at: April 10, 2026, 1:37 p.m.