Triple

T14440594
Position Surface form Disambiguated ID Type / Status
Subject Apache Software Foundation E358073 entity
Predicate governs P760 FINISHED
Object Apache Cassandra E358077 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 Cassandra | Statement: [Apache Software Foundation, governs, Apache Cassandra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Apache Cassandra
Context triple: [Apache Software Foundation, governs, Apache Cassandra]
  • A. Apache Cassandra chosen
    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.
  • B. Apache Cassandra CQL
    Apache Cassandra CQL is a SQL-like query language used to define, query, and manage data stored in Apache Cassandra distributed databases.
  • C. Apache HBase
    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.
  • D. ScyllaDB
    ScyllaDB is a high-performance, distributed NoSQL database designed as a drop-in replacement for Apache Cassandra, optimized for low latency and high throughput.
  • E. 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.
  • 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_69d8279402a88190821ffa39ae15bccf completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de914c1398819090fa2a74d257ba3e completed April 14, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd5bda6ee88190aeec77092eb3576a completed May 8, 2026, 3:43 a.m.
Created at: April 10, 2026, 1:18 a.m.