Triple

T7985773
Position Surface form Disambiguated ID Type / Status
Subject Apache Oozie E185677 entity
Predicate partOf P40 FINISHED
Object Apache Hadoop ecosystem E702631 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 Hadoop ecosystem | Statement: [Apache Oozie, partOf, Apache Hadoop ecosystem]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Apache Hadoop ecosystem
Context triple: [Apache Oozie, partOf, Apache Hadoop ecosystem]
  • A. Apache ecosystem chosen
    The Apache ecosystem is a broad collection of open-source software projects under the Apache Software Foundation that provide scalable, enterprise-grade tools for web servers, big data processing, machine learning, and more.
  • 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 Spark
    Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
  • D. 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.
  • E. 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.
  • 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_69cb3c4b87e48190a797f5363c8f0a04 completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc938ffbb481908c4540e560469777 completed April 1, 2026, 3:40 a.m.
Created at: March 30, 2026, 5:15 p.m.