Triple
T17500137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Optimized Row Columnar |
E426163
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Apache Impala |
—
|
NE NERFINISHED |
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: Apache Impala | Statement: [Optimized Row Columnar, integratesWith, Apache Impala]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Impala Context triple: [Optimized Row Columnar, integratesWith, Apache Impala]
-
A.
Apache Impala
chosen
Apache Impala is a massively parallel, SQL-on-Hadoop query engine designed for low-latency, interactive analysis of large-scale data stored in distributed systems.
-
B.
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.
-
C.
IMPALA
IMPALA is a scalable deep reinforcement learning architecture designed for efficient distributed training of agents across many tasks and environments.
-
D.
Hive
The Hive is the Zerg’s ultimate tech structure in StarCraft, enabling advanced units, upgrades, and late-game capabilities.
-
E.
Greenplum
Greenplum is a massively parallel, open-source data warehouse and analytics platform designed for large-scale business intelligence and big data workloads.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889dd9164819087b1dc3c9240c870 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e452112ff0819089c2951baba90102 |
completed | April 19, 2026, 3:54 a.m. |
Created at: April 10, 2026, 5:48 a.m.