Triple
T17561432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cloud Datastore |
E427702
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Cloud Run |
—
|
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: Cloud Run | Statement: [Cloud Datastore, integratesWith, Cloud Run]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cloud Run Context triple: [Cloud Datastore, integratesWith, Cloud Run]
-
A.
Cloud Run
chosen
Cloud Run is a fully managed serverless compute platform from Google Cloud that runs containerized applications and APIs that scale automatically on demand.
-
B.
Cloud Functions
Cloud Functions is Google Cloud’s serverless compute platform for running event-driven code without managing servers.
-
C.
Knative
Knative is an open-source Kubernetes-based platform that simplifies building, deploying, and managing serverless and event-driven applications.
-
D.
AWS App Runner
AWS App Runner is a fully managed service from Amazon Web Services that makes it easy to build, deploy, and run containerized web applications and APIs at scale without managing infrastructure.
-
E.
Google Kubernetes Engine
Google Kubernetes Engine is a managed Kubernetes service that lets users deploy, manage, and scale containerized applications on Google Cloud infrastructure.
- 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_69d889e0385081908a04b66f4dd4bd0d |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e456274c888190ac80402e391674dd |
completed | April 19, 2026, 4:12 a.m. |
Created at: April 10, 2026, 5:50 a.m.