Triple
T18300486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ray |
E438345
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | Ray RLlib |
—
|
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: Ray RLlib | Statement: [Ray, hasComponent, Ray RLlib]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ray RLlib Context triple: [Ray, hasComponent, Ray RLlib]
-
A.
RLlib
chosen
RLlib is a scalable, open-source reinforcement learning library built on Ray that provides high-level APIs and distributed training support for a wide range of RL algorithms.
-
B.
TF-Agents
TF-Agents is an open-source library built on TensorFlow that provides modular components and tools for developing, training, and evaluating reinforcement learning algorithms.
-
C.
Horovod
Horovod is an open-source distributed deep learning framework designed to make training models across multiple GPUs and machines fast and easy.
-
D.
ChainerRL
ChainerRL is a reinforcement learning library built on top of the Chainer deep learning framework, providing tools and algorithms for training and evaluating RL agents.
-
E.
Turbo RL
Turbo RL is a long-wheelbase luxury performance sedan variant of Bentley's Turbo R, offering enhanced rear passenger space and comfort.
- 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_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.