Triple
T8093930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apache Flink |
E188935
|
entity |
| Predicate | integratesWith |
P1075
|
FINISHED |
| Object | Apache Hive |
E185675
|
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: Apache Hive | Statement: [Apache Flink, integratesWith, Apache Hive]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Apache Hive Context triple: [Apache Flink, integratesWith, Apache Hive]
-
A.
Apache Hive
chosen
Apache Hive is a data warehouse and SQL-like query system built on top of Hadoop for managing and analyzing large datasets stored in distributed storage.
-
B.
Apache Pig
Apache Pig is a high-level platform for creating MapReduce programs used to analyze large data sets in the Hadoop ecosystem.
-
C.
Apache Sqoop
Apache Sqoop is an open-source tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases.
-
D.
HiveQL
HiveQL is a SQL-like query language designed for managing and analyzing large datasets stored in Apache Hive’s data warehouse system on Hadoop.
-
E.
Hive Metastore
Hive Metastore is a central metadata repository service that stores and manages schema and table information for data warehousing systems like Apache Hive.
- 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_69ca82b7b3e88190b9041ab0ef28b3cb |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb429089cc81909e4625f9cc7e305f |
completed | March 31, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc93ff6a108190ac60218ec2716c60 |
completed | April 1, 2026, 3:41 a.m. |
Created at: March 30, 2026, 5:30 p.m.