Triple
T19749825
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Object Storage Service |
E474346
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Alibaba Cloud Data Lake services |
—
|
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: Alibaba Cloud Data Lake services | Statement: [Object Storage Service, integratesWith, Alibaba Cloud Data Lake services]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alibaba Cloud Data Lake services Context triple: [Object Storage Service, integratesWith, Alibaba Cloud Data Lake services]
-
A.
Azure Data Lake Storage
Azure Data Lake Storage is a scalable, secure cloud-based data lake service from Microsoft designed for big data analytics and enterprise data warehousing workloads.
-
B.
MongoDB Atlas Data Lake
MongoDB Atlas Data Lake is a fully managed cloud service that lets users query and analyze data across cloud object storage and MongoDB databases using the MongoDB query language without complex data movement or transformation.
-
C.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
D.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
E.
Alibaba Cloud Log Service
Alibaba Cloud Log Service is a fully managed, scalable logging and observability platform for collecting, storing, analyzing, and visualizing log data within the Alibaba Cloud ecosystem.
- 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: Alibaba Cloud Data Lake services Target entity description: Alibaba Cloud Data Lake services are a suite of cloud-native tools and platforms on Alibaba Cloud for storing, managing, and analyzing large-scale structured and unstructured data in a centralized data lake architecture.
-
A.
Alibaba Cloud DataWorks
chosen
Alibaba Cloud DataWorks is a fully managed, cloud-native data integration, development, and governance platform that supports building and orchestrating complex data pipelines and analytics workflows on Alibaba Cloud.
-
B.
Azure Data Lake Storage
Azure Data Lake Storage is a scalable, secure cloud-based data lake service from Microsoft designed for big data analytics and enterprise data warehousing workloads.
-
C.
MongoDB Atlas Data Lake
MongoDB Atlas Data Lake is a fully managed cloud service that lets users query and analyze data across cloud object storage and MongoDB databases using the MongoDB query language without complex data movement or transformation.
-
D.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
E.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
- F. None of above.
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_69d8e51940a0819087bd2996f98da668 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6529875688190952af476aa5be492 |
completed | April 20, 2026, 4:21 p.m. |
Created at: April 10, 2026, 1:47 p.m.