Triple

T23154534
Position Surface form Disambiguated ID Type / Status
Subject Eclipse Vert.x E578403 entity
Predicate supportsStandard P1587 FINISHED
Object Reactive Streams NE NERFINISHED

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: Reactive Streams | Statement: [Eclipse Vert.x, supportsStandard, Reactive Streams]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reactive Streams
Context triple: [Eclipse Vert.x, supportsStandard, Reactive Streams]
  • A. Reactive Streams chosen
    Reactive Streams is a standard for asynchronous stream processing with non-blocking back pressure, designed to enable interoperable reactive programming across different libraries and runtimes.
  • B. Akka Streams
    Akka Streams is a library for building and running asynchronous, backpressure-aware data processing pipelines on the JVM, based on the Reactive Streams specification.
  • C. Spring WebFlux
    Spring WebFlux is a reactive, non-blocking web framework in the Spring ecosystem designed for building scalable, event-driven web applications and APIs.
  • D. RxJava
    RxJava is a Java library for composing asynchronous and event-based programs using observable sequences and functional reactive programming principles.
  • E. Kafka Streams
    Kafka Streams is a Java library for building real-time, distributed stream processing applications on top of Apache Kafka.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e245fb8de081908f0eba7b5fd75bc4 completed April 17, 2026, 2:38 p.m.
NER Named-entity recognition batch_69f18efbe9a08190bcb6e822b8eab544 completed April 29, 2026, 4:54 a.m.
Created at: April 17, 2026, 4:01 p.m.