Triple
T15313027
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | AI2-THOR |
E366086
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | AI research tool |
C36260
|
CONCEPT FINISHED |
How this triple was built (1 step)
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.
CD
Concept disambiguation
gpt-5-mini-2025-08-07
Target class: AI research tool Context triple: [AI2-THOR, instanceOf, AI research tool]
-
A.
machine learning research institute
A machine learning research institute is an organization dedicated to advancing the theory, algorithms, and applications of machine learning through systematic research, experimentation, and collaboration.
-
B.
intelligence research agency
An intelligence research agency is an organization dedicated to systematically collecting, analyzing, and interpreting information to produce actionable insights for strategic, security, or policy decision-making.
-
C.
generative AI service suite
A generative AI service suite is an integrated collection of tools and APIs that create, transform, and analyze content (such as text, images, code, or audio) using advanced machine learning models to support diverse applications and workflows.
-
D.
natural language understanding platform
A natural language understanding platform is a system that interprets, analyzes, and derives meaning from human language input to enable intelligent, context-aware interactions and automation.
-
E.
test of machine intelligence
A test of machine intelligence is a systematic procedure or set of tasks designed to evaluate a machine's ability to exhibit behaviors or problem-solving capabilities that are typically associated with human cognitive processes.
- F. None of above. chosen
Provenance (1 batch)
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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
Created at: April 10, 2026, 3:16 a.m.