Triple
T15989632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cloudera |
E387790
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | Cloudera Data Warehouse |
E387790
|
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: Cloudera Data Warehouse | Statement: [Cloudera, product, Cloudera Data Warehouse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cloudera Data Warehouse Context triple: [Cloudera, product, Cloudera Data Warehouse]
-
A.
Cloudera
chosen
Cloudera is an enterprise data management and analytics company best known for its platform built on Apache Hadoop and related open-source big data technologies.
-
B.
Vertica
Vertica is a high-performance, column-oriented analytical database system designed for large-scale data warehousing and real-time analytics.
-
C.
Tamr
Tamr is a data mastering and integration company that uses machine learning to unify and clean large, disparate datasets for enterprises.
-
D.
Amazon Redshift
Amazon Redshift is a fully managed, cloud-based data warehousing service from Amazon Web Services designed for fast querying and analysis of large datasets using SQL.
-
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 (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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157829ec08190aa4a683e29a0148a |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf1cb1388190b1ebccc6705e5974 |
completed | May 10, 2026, 12:19 a.m. |
Created at: April 10, 2026, 4:54 a.m.