Triple
T18300782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Farama Foundation |
E438351
|
entity |
| Predicate | product |
P490
|
FINISHED |
| Object | Farama-Docs |
—
|
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: Farama-Docs | Statement: [Farama Foundation, product, Farama-Docs]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Farama-Docs Context triple: [Farama Foundation, product, Farama-Docs]
-
A.
Farama Foundation
The Farama Foundation is an organization that develops and maintains open-source reinforcement learning tools and libraries for the research and engineering community.
-
B.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
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.
-
E.
AI2-THOR
AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
- 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: Farama-Docs Target entity description: Farama-Docs is the centralized documentation hub maintained by the Farama Foundation for its reinforcement learning and related open-source libraries.
-
A.
Farama Foundation
chosen
The Farama Foundation is an organization that develops and maintains open-source reinforcement learning tools and libraries for the research and engineering community.
-
B.
OpenAI Gym
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms through a standardized collection of environments and interfaces.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
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.
-
E.
AI2-THOR
AI2-THOR is an interactive 3D simulation platform designed for training and evaluating embodied AI agents in visually rich, physics-enabled environments.
- F. None of above.
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_69e5017f63dc819083a675d570620f2f |
completed | April 19, 2026, 4:23 p.m. |
Created at: April 10, 2026, 10:35 a.m.