Triple
T17500164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Trino |
E426164
|
entity |
| Predicate | previousName |
P65
|
FINISHED |
| Object | PrestoSQL |
—
|
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: PrestoSQL | Statement: [Trino, previousName, PrestoSQL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PrestoSQL Context triple: [Trino, previousName, PrestoSQL]
-
A.
BlazingSQL
BlazingSQL is an open-source SQL engine that enables GPU-accelerated data processing and analytics, often used within the NVIDIA RAPIDS ecosystem for high-performance query execution on large datasets.
-
B.
Greenplum
Greenplum is a massively parallel, open-source data warehouse and analytics platform designed for large-scale business intelligence and big data workloads.
-
C.
Apache Impala
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.
-
D.
Vertica
Vertica is a high-performance, column-oriented analytical database system designed for large-scale data warehousing and real-time analytics.
-
E.
Presto
chosen
Presto is an open-source, distributed SQL query engine designed for fast, interactive analytics on large-scale data from multiple sources.
- 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.