Triple

T7985470
Position Surface form Disambiguated ID Type / Status
Subject YARN E185672 entity
Predicate supportsFramework P9089 FINISHED
Object Apache Storm (via integration) E185674 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 Storm (via integration) | Statement: [YARN, supportsFramework, Apache Storm (via integration)]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Apache Storm (via integration)
Context triple: [YARN, supportsFramework, Apache Storm (via integration)]
  • A. Apache Storm chosen
    Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
  • B. IBM Streams
    IBM Streams is a high-performance stream processing platform that enables real-time ingestion, analysis, and correlation of large-scale data in motion for enterprise applications.
  • C. Apache Kafka
    Apache Kafka is a distributed event streaming platform widely used for building real-time data pipelines and streaming applications.
  • D. Apache Flink
    Apache Flink is an open-source distributed stream-processing framework designed for high-throughput, low-latency data processing and real-time analytics on large-scale data.
  • E. Apache Flume
    Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log and event data into Hadoop and other data stores.
  • 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_69cbe0e6f3c48190a0132fa90eec6420 completed March 31, 2026, 2:57 p.m.
Created at: March 30, 2026, 5:15 p.m.