Triple
T19749991
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AnalyticDB |
E474349
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Alibaba Cloud Realtime Compute |
—
|
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 Realtime Compute | Statement: [AnalyticDB, integratesWith, Alibaba Cloud Realtime Compute]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alibaba Cloud Realtime Compute Context triple: [AnalyticDB, integratesWith, Alibaba Cloud Realtime Compute]
-
A.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is a fully managed AWS service that enables real-time processing and analysis of streaming data using SQL or Apache Flink.
-
B.
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.
-
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.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for developing and executing batch and streaming data processing pipelines, based on Apache Beam, within the Google Cloud ecosystem.
-
E.
Azure Stream Analytics
Azure Stream Analytics is a real-time analytics and complex event processing service in Microsoft Azure that ingests and analyzes streaming data from various sources to generate timely insights and actions.
- 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 Realtime Compute Target entity description: Alibaba Cloud Realtime Compute is a real-time stream processing and analytics service that enables low-latency data processing and event-driven applications within the Alibaba Cloud ecosystem.
-
A.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is a fully managed AWS service that enables real-time processing and analysis of streaming data using SQL or Apache Flink.
-
B.
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.
-
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.
Google Cloud Dataflow
Google Cloud Dataflow is a fully managed service for developing and executing batch and streaming data processing pipelines, based on Apache Beam, within the Google Cloud ecosystem.
-
E.
Azure Stream Analytics
Azure Stream Analytics is a real-time analytics and complex event processing service in Microsoft Azure that ingests and analyzes streaming data from various sources to generate timely insights and actions.
- 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_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.