Triple
T18705599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Parquet |
E457357
|
entity |
| Predicate | usedWith |
P4791
|
FINISHED |
| Object | Databricks |
—
|
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: Databricks | Statement: [Apache Parquet, usedWith, Databricks]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Databricks Context triple: [Apache Parquet, usedWith, Databricks]
-
A.
Databricks
chosen
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
B.
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.
-
C.
Anyscale
Anyscale is a company that builds tools and infrastructure to simplify and scale distributed computing and AI applications in the cloud.
-
D.
Tamr
Tamr is a data mastering and integration company that uses machine learning to unify and clean large, disparate datasets for enterprises.
-
E.
MongoDB Atlas Data Lake
MongoDB Atlas Data Lake is a fully managed cloud service that lets users query and analyze data across cloud object storage and MongoDB databases using the MongoDB query language without complex data movement or transformation.
- 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_69e5671717b88190974f542015f641e8 |
completed | April 19, 2026, 11:36 p.m. |
Created at: April 10, 2026, 11:49 a.m.