Triple
T7985843
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Sqoop |
E185679
|
entity |
| Predicate | supportsTarget |
P5090
|
FINISHED |
| Object | Hive |
E185675
|
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: Hive | Statement: [Apache Sqoop, supportsTarget, Hive]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hive Context triple: [Apache Sqoop, supportsTarget, Hive]
-
A.
Apache Hive
chosen
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.
-
B.
IMPALA
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
-
C.
Lakehouse
"Lakehouse" is a song by the Icelandic indie folk/pop band Of Monsters and Men, known for its atmospheric sound and emotive storytelling.
-
D.
Tableau
Tableau is a widely used data visualization and business intelligence software platform that enables users to analyze, explore, and present data through interactive dashboards and reports.
-
E.
Hive Mind
Hive Mind is a science fiction novel by The Internet that explores themes of collective consciousness and interconnected intelligence in a digitally linked future.
- 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_69cb3c4b87e48190a797f5363c8f0a04 |
completed | March 31, 2026, 3:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cbe0e6f3c48190a0132fa90eec6420 |
completed | March 31, 2026, 2:57 p.m. |
Created at: March 30, 2026, 5:15 p.m.