Triple
T17499389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SPICE |
E426152
|
entity |
| Predicate | compatibleWith |
P203
|
FINISHED |
| Object | Amazon Redshift |
—
|
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: Amazon Redshift | Statement: [SPICE, compatibleWith, Amazon Redshift]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon Redshift Context triple: [SPICE, compatibleWith, Amazon Redshift]
-
A.
Amazon Redshift
chosen
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.
-
B.
Amazon Athena
Amazon Athena is a serverless, interactive query service from AWS that lets users analyze data directly in Amazon S3 using standard SQL.
-
C.
Amazon Aurora
Amazon Aurora is a fully managed, cloud-native relational database engine from AWS designed for high performance, scalability, and compatibility with MySQL and PostgreSQL.
-
D.
Amazon RDS
Amazon RDS is a managed relational database service by Amazon Web Services that simplifies setup, operation, and scaling of databases in the cloud.
-
E.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
- 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_69e4521028048190aa7c4023a72a12f4 |
completed | April 19, 2026, 3:54 a.m. |
Created at: April 10, 2026, 5:48 a.m.