Triple
T28234255
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SQL API (Apache Flink) |
E711834
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | component of Apache Flink |
C53358
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: component of Apache Flink Context triple: [SQL API (Apache Flink), instanceOf, component of Apache Flink]
-
A.
component of Apache Storm
A component of Apache Storm is a modular processing unit—such as a spout or bolt—that participates in a real-time, distributed computation topology by emitting, transforming, or aggregating streaming data tuples.
-
B.
component of Apache Hive
A component of Apache Hive is a modular subsystem—such as the metastore, query engine, or storage handler—that collaborates with other parts of Hive to translate, optimize, and execute SQL-like queries over distributed data.
-
C.
Flume sink component
A Flume sink component is a pluggable endpoint in an Apache Flume data flow that reliably delivers events from a channel to an external storage or service, such as HDFS, HBase, or a custom destination.
-
D.
data-parallel execution engine
chosen
A data-parallel execution engine is a system that coordinates the simultaneous processing of independent data partitions across multiple compute resources to accelerate large-scale computations.
-
E.
in-memory analytics engine
An in-memory analytics engine is a software system that stores and processes data primarily in main memory to deliver extremely fast analytical queries and real-time insights.
- F. None of above.
Provenance (1 batch)
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_69efb51ece308190b8c269a057e36652 |
completed | April 27, 2026, 7:12 p.m. |
Created at: April 27, 2026, 10:53 p.m.