Triple

T18300520
Position Surface form Disambiguated ID Type / Status
Subject Ray Tune E438346 entity
Predicate basedOn P98 FINISHED
Object Ray distributed computing framework NE NERFINISHED

How this triple was built (3 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: Ray distributed computing framework | Statement: [Ray Tune, basedOn, Ray distributed computing framework]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Ray distributed computing framework
Context triple: [Ray Tune, basedOn, Ray distributed computing framework]
  • A. Apache Storm
    Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
  • B. Distributed Resource Management Application API
    Distributed Resource Management Application API is a standardized programming interface that allows applications to submit and control jobs on distributed resource management and grid computing systems.
  • C. parallel distributed processing
    Parallel distributed processing is a cognitive and computational framework in which mental processes emerge from the simultaneous activity of many simple, interconnected processing units, often implemented as neural networks.
  • D. Apache Mesos
    Apache Mesos is an open-source cluster manager that abstracts CPU, memory, storage, and other resources away from machines to enable efficient deployment and scaling of distributed applications and frameworks.
  • E. Distributed Hash Table
    A Distributed Hash Table (DHT) is a decentralized system that distributes key–value storage and lookup responsibilities across many nodes, enabling scalable and fault-tolerant data location without a central server.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Ray distributed computing framework
Target entity description: Ray distributed computing framework is an open-source system for scaling Python applications that provides simple primitives for distributed execution and supports building and running large-scale machine learning and data processing workloads.
  • A. Apache Storm
    Apache Storm is a distributed real-time computation system designed for processing large streams of data with low latency and high fault tolerance.
  • B. Distributed Resource Management Application API
    Distributed Resource Management Application API is a standardized programming interface that allows applications to submit and control jobs on distributed resource management and grid computing systems.
  • C. parallel distributed processing
    Parallel distributed processing is a cognitive and computational framework in which mental processes emerge from the simultaneous activity of many simple, interconnected processing units, often implemented as neural networks.
  • D. Apache Mesos
    Apache Mesos is an open-source cluster manager that abstracts CPU, memory, storage, and other resources away from machines to enable efficient deployment and scaling of distributed applications and frameworks.
  • E. Distributed Hash Table
    A Distributed Hash Table (DHT) is a decentralized system that distributes key–value storage and lookup responsibilities across many nodes, enabling scalable and fault-tolerant data location without a central server.
  • F. None of above. chosen

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_69d8b915e3e881909125d760c15d0c29 completed April 10, 2026, 8:47 a.m.
NER Named-entity recognition batch_69e5017e88cc8190a969eb628ca1b496 completed April 19, 2026, 4:23 p.m.
Created at: April 10, 2026, 10:35 a.m.