Triple
T34588445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gilgamesh Wulfenbach |
E888109
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | Spark |
C11253
|
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: Spark Context triple: [Gilgamesh Wulfenbach, instanceOf, Spark]
-
A.
snowpark
Snowpark is a high-level API and developer framework in Snowflake that lets you write data pipelines, transformations, and machine learning logic in languages like Python, Java, and Scala directly within the Snowflake data platform.
-
B.
big data framework
chosen
A big data framework is a software platform that enables the distributed storage, processing, and analysis of large-scale, complex datasets across clusters of machines.
-
C.
Apache Pig component
An Apache Pig component is a modular element within the Pig data flow (such as a loader, transformer, or storage function) that defines how data is read, processed, or written in large-scale parallel data analysis tasks.
-
D.
core abstraction in Apache Flink
A core abstraction in Apache Flink is a fundamental programming or data model construct (such as DataStream or DataSet) that represents distributed data and the operations applied to it, enabling scalable, fault-tolerant stream and batch processing.
-
E.
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.
- 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_69f349d3bfcc81909874c99e646fb3ea |
completed | April 30, 2026, 12:23 p.m. |
Created at: May 1, 2026, 2:03 a.m.