Triple
T7984807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Spark |
E185661
|
entity |
| Predicate | component |
P35
|
FINISHED |
| Object | SparkR |
E185661
|
NE FINISHED |
How this triple was built (2 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: SparkR | Statement: [Apache Spark, component, SparkR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SparkR Context triple: [Apache Spark, component, SparkR]
-
A.
Spark
"Spark" is a virtuosic jazz fusion composition by Japanese pianist Hiromi Uehara, showcasing her signature blend of technical brilliance and energetic, genre-blurring style.
-
B.
Apache Spark
chosen
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
-
C.
PySpark
PySpark is the Python API for Apache Spark, enabling large-scale data processing, analysis, and machine learning using Python.
-
D.
Scala (via Snowpark)
Scala (via Snowpark) is a way to use the Scala programming language within Snowflake’s Snowpark developer framework to build and run data pipelines, transformations, and applications directly in the Snowflake Data Cloud.
-
E.
Apache Hive
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ca829a2cfc819083d591d58ec04075 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3c4a55b881909a96133e56c0dffa |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc567cfe548190bbd163a32c340bcc |
completed | March 31, 2026, 11:19 p.m. |
Created at: March 30, 2026, 5:15 p.m.