Triple
T18705445
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Airflow |
E457354
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | BigQuery |
—
|
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: BigQuery | Statement: [Apache Airflow, integratesWith, BigQuery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: BigQuery Context triple: [Apache Airflow, integratesWith, BigQuery]
-
A.
Google BigQuery
chosen
Google BigQuery is a fully managed, serverless cloud data warehouse from Google Cloud designed for fast SQL-based analytics on large-scale datasets.
-
B.
Bigtable
Bigtable is Google's distributed, scalable NoSQL database designed to handle massive amounts of structured data with high performance and reliability.
-
C.
Cloud Spanner
Cloud Spanner is Google Cloud’s fully managed, horizontally scalable, globally distributed relational database service that offers strong consistency and high availability.
-
D.
Data Studio
Data Studio is Google's free data visualization and business intelligence tool that lets users create interactive, shareable reports and dashboards from multiple data sources.
-
E.
BigQueryExampleGen
BigQueryExampleGen is a TFX ExampleGen component variant that reads training examples directly from Google BigQuery for use in TensorFlow Extended pipelines.
- 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_69d8d392aad081909fe31aa03e6e97d1 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e5671665bc8190b9b4a4ce4ec5b2eb |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 10, 2026, 11:49 a.m.