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.