Triple
T4280105
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Amazon QuickSight |
E97125
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Amazon S3 |
E100305
|
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: Amazon S3 | Statement: [Amazon QuickSight, integratesWith, Amazon S3]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Amazon S3 Context triple: [Amazon QuickSight, integratesWith, Amazon S3]
-
A.
Amazon S3
chosen
Amazon S3 is a scalable, highly durable cloud object storage service from Amazon Web Services used for storing and retrieving large amounts of data over the internet.
-
B.
Google Cloud Storage
Google Cloud Storage is a scalable, durable, and secure object storage service for storing and accessing data on Google Cloud infrastructure.
-
C.
Amazon EFS
Amazon EFS is a fully managed, scalable, cloud-native file storage service that provides shared, elastic file systems for use with AWS compute resources.
-
D.
Azure Blob Storage
Azure Blob Storage is a cloud-based object storage service for storing and managing large amounts of unstructured data such as text and binary files.
-
E.
AWS
AWS is a train protection and warning system used on railways to alert drivers to signal aspects and speed restrictions, enhancing operational safety.
- 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_69b34544be3c819084d1ab82d29f90c5 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b350367da48190b735deef9b5d2d2e |
completed | March 12, 2026, 11:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b5e4e7a7cc8190a2ffc15c236f80d5 |
completed | March 14, 2026, 10:44 p.m. |
Created at: March 12, 2026, 11:07 p.m.