Triple
T18800809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AWS CDK |
E459748
|
entity |
| Predicate | supportsService |
P203
|
FINISHED |
| Object | Amazon Aurora |
—
|
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 Aurora | Statement: [AWS CDK, supportsService, Amazon Aurora]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon Aurora Context triple: [AWS CDK, supportsService, Amazon Aurora]
-
A.
Amazon Aurora
chosen
Amazon Aurora is a fully managed, cloud-native relational database engine from AWS designed for high performance, scalability, and compatibility with MySQL and PostgreSQL.
-
B.
Amazon Neptune
Amazon Neptune is a fully managed graph database service designed for storing and querying highly connected data using popular graph models and query languages.
-
C.
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.
-
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.
Amazon DynamoDB
Amazon DynamoDB is a fully managed, serverless NoSQL database service by AWS designed for high-performance, scalable key-value and document data storage.
- 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_69d8d398c7d4819091cb2f7e48948aeb |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5a02332d88190b68feea7f2f86d06 |
completed | April 20, 2026, 3:40 a.m. |
Created at: April 10, 2026, 11:53 a.m.