Triple
T36489136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | connectionism |
E899009
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | approach in artificial intelligence |
C28693
|
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: approach in artificial intelligence Context triple: [connectionism, instanceOf, approach in artificial intelligence]
-
A.
artificial intelligence
Artificial intelligence is a field of computer science focused on creating systems that can perform tasks that typically require human intelligence, such as learning, reasoning, perception, and decision-making.
-
B.
theory in artificial intelligence
chosen
A theory in artificial intelligence is a systematic, formal framework that explains, predicts, or guides the design of intelligent behavior in machines by defining underlying principles, models, and assumptions.
-
C.
benchmark in artificial intelligence
A benchmark in artificial intelligence is a standardized task, dataset, or evaluation protocol used to quantitatively compare and assess the performance of AI models and algorithms.
-
D.
artificial intelligence framework
An artificial intelligence framework is a structured software environment that provides tools, libraries, and interfaces to design, train, deploy, and manage AI and machine learning models efficiently.
-
E.
pioneer in artificial intelligence
A pioneer in artificial intelligence is an individual or organization that makes groundbreaking, foundational contributions to the theories, technologies, and applications that advance the field of AI.
- F. None of above.
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_69f76e5ad4588190bdbce60c52fbb785 |
completed | May 3, 2026, 3:48 p.m. |
Created at: May 3, 2026, 4:10 p.m.