Triple

T7931989
Position Surface form Disambiguated ID Type / Status
Subject App Engine E184209 entity
Predicate integratesWith P1075 FINISHED
Object Cloud Datastore E427702 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: Cloud Datastore | Statement: [App Engine, integratesWith, Cloud Datastore]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Cloud Datastore
Context triple: [App Engine, integratesWith, Cloud Datastore]
  • A. Cloud Datastore chosen
    Cloud Datastore is a highly scalable, fully managed NoSQL document database service provided by Google Cloud for building and running web and mobile applications.
  • B. Cloud Spanner
    Cloud Spanner is Google Cloud’s fully managed, horizontally scalable, globally distributed relational database service that offers strong consistency and high availability.
  • C. Bigtable
    Bigtable is Google's distributed, scalable NoSQL database designed to handle massive amounts of structured data with high performance and reliability.
  • D. Cloud SQL
    Cloud SQL is Google Cloud’s fully managed relational database service for running MySQL, PostgreSQL, and SQL Server workloads in the cloud.
  • E. Firestore
    Firestore is a scalable, fully managed NoSQL document database service from Google Cloud designed for real-time data synchronization and serverless application development.
  • 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_69ca8290c21c8190906a5ca6fe2b03c4 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3ace87f081908635769942645e78 completed March 31, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5c041e588190bfbf251ed88d5bcd completed March 31, 2026, 5:30 a.m.
Created at: March 30, 2026, 5:07 p.m.