Triple
T12682852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aqua Data Studio |
E302989
|
entity |
| Predicate | supportsDatabase |
P11254
|
FINISHED |
| Object | Vertica |
E991161
|
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: Vertica | Statement: [Aqua Data Studio, supportsDatabase, Vertica]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Vertica Context triple: [Aqua Data Studio, supportsDatabase, Vertica]
-
A.
Vertica
chosen
Vertica is a high-performance, column-oriented analytical database system designed for large-scale data warehousing and real-time analytics.
-
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.
Teradata
Teradata is an enterprise-grade relational database management system and data warehousing platform designed for large-scale analytics and business intelligence workloads.
-
D.
VoltDB
VoltDB is a high-performance, in-memory, distributed SQL database designed for real-time analytics and transaction processing at massive scale.
-
E.
Tamr
Tamr is a data mastering and integration company that uses machine learning to unify and clean large, disparate datasets for enterprises.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d68358819095bdaab8adf1dcf0 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f671a733a48190b55d296573c86eaf |
completed | May 2, 2026, 9:50 p.m. |
Created at: April 9, 2026, 5:21 p.m.