Triple

T7985571
Position Surface form Disambiguated ID Type / Status
Subject Apache Storm E185674 entity
Predicate supportsIntegrationWith P5090 FINISHED
Object Apache HBase E185676 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 HBase | Statement: [Apache Storm, supportsIntegrationWith, Apache HBase]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Apache HBase
Context triple: [Apache Storm, supportsIntegrationWith, Apache HBase]
  • A. Apache HBase chosen
    Apache HBase is a distributed, scalable, NoSQL database designed for real-time read/write access to large datasets, typically running on top of the Hadoop ecosystem.
  • B. Hadoop
    Hadoop is an open-source framework that enables distributed storage and parallel processing of large data sets across clusters of commodity hardware.
  • C. Apache Hive
    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.
  • D. Apache Cassandra
    Apache Cassandra is a highly scalable, distributed NoSQL database designed for handling large amounts of data across many commodity servers with high availability and no single point of failure.
  • E. Bigtable
    Bigtable is Google's distributed, scalable NoSQL database designed to handle massive amounts of structured data with high performance and reliability.
  • 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_69ca829a2cfc819083d591d58ec04075 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c4a55b881909a96133e56c0dffa completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc567cfe548190bbd163a32c340bcc completed March 31, 2026, 11:19 p.m.
Created at: March 30, 2026, 5:15 p.m.