Triple
T4416693
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Programs with Common Sense |
E94991
|
entity |
| Predicate | instanceOf |
P0
|
FINISHED |
| Object | artificial intelligence paper |
C427
|
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: artificial intelligence paper Context triple: [Programs with Common Sense, instanceOf, artificial intelligence paper]
-
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.
landmark paper in machine learning
A landmark paper in machine learning is a highly influential publication that introduces foundational theories, algorithms, or empirical results that significantly shape subsequent research and practice in the field.
-
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.
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.
-
E.
scientific paper
chosen
A scientific paper is a structured, peer-oriented document that reports original research, methods, analyses, and conclusions to advance knowledge within a specific academic or scientific field.
- 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_69b3453a36908190b95a79a297ca083c |
completed | March 12, 2026, 10:59 p.m. |
Created at: March 12, 2026, 11:29 p.m.