Triple
T2139429
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jeff Dean |
E46726
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Spanner |
E184215
|
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: Spanner | Statement: [Jeff Dean, notableWork, Spanner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Spanner Context triple: [Jeff Dean, notableWork, Spanner]
-
A.
Cloud Spanner
chosen
Cloud Spanner is Google Cloud’s fully managed, horizontally scalable, globally distributed relational database service that offers strong consistency and high availability.
-
B.
Bigtable
Bigtable is Google's distributed, scalable NoSQL database designed to handle massive amounts of structured data with high performance and reliability.
-
C.
Snowflake Data Cloud
Snowflake Data Cloud is a cloud-native data platform that enables organizations to store, integrate, and analyze data at scale across multiple clouds with a unified, fully managed service.
-
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.
Google BigQuery
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
- 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_69a88a174ab48190a5db20c132e5dccf |
completed | March 4, 2026, 7:37 p.m. |
| NER | Named-entity recognition | batch_69abbe025d3c81908bcb33a7ff09eae8 |
completed | March 7, 2026, 5:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae51b1290c8190a08850b428c99a6c |
completed | March 9, 2026, 4:50 a.m. |
Created at: March 4, 2026, 7:44 p.m.