Triple
T4600408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon S3 |
E100305
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | AWS Glue |
E97123
|
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: AWS Glue | Statement: [Amazon S3, integratesWith, AWS Glue]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: AWS Glue Context triple: [Amazon S3, integratesWith, AWS Glue]
-
A.
AWS Glue
chosen
AWS Glue is a fully managed extract, transform, and load (ETL) service from Amazon Web Services that simplifies data preparation and integration for analytics and data warehousing.
-
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 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.
-
D.
Amazon Kinesis Data Analytics
Amazon Kinesis Data Analytics is a fully managed AWS service that enables real-time processing and analysis of streaming data using SQL or Apache Flink.
-
E.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning 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_69bd43cbc014819098b45f435908f88a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5971f448819090f6e76c7d3ffc2d |
completed | March 20, 2026, 2:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfa5a7aac8190b540b80816d55051 |
completed | March 21, 2026, 1:54 a.m. |
Created at: March 20, 2026, 1:11 p.m.