Triple
T14383597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuart Card |
E356668
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Model Human Processor |
E874568
|
NE FINISHED |
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: Model Human Processor | Statement: [Stuart Card, notableWork, Model Human Processor]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Model Human Processor Context triple: [Stuart Card, notableWork, Model Human Processor]
-
A.
Model Human Processor
chosen
Model Human Processor is a cognitive engineering framework that models human perception, cognition, and motor behavior as an information-processing system to predict and improve human–computer interaction performance.
-
B.
Goya inference processor
The Goya inference processor is Habana Labs’ specialized AI chip designed to accelerate deep learning inference workloads with high performance and efficiency.
-
C.
Human Understanding
Human Understanding is a philosophical work by Stephen Toulmin that examines the nature of human rationality, reasoning, and the development of knowledge.
-
D.
Intelligence Processing Unit
The Intelligence Processing Unit is a specialized processor architecture designed by Graphcore to accelerate artificial intelligence and machine learning workloads with highly parallel, memory-rich compute.
-
E.
GOMS model of human–computer interaction
The GOMS model of human–computer interaction is a cognitive modeling framework that predicts user performance by decomposing tasks into goals, operators, methods, and selection rules.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d827927c988190ad98bb0360981783 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de900d28c88190a37feee4743563de |
completed | April 14, 2026, 7:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bc0c9708190b5025e9675e0e925 |
completed | May 8, 2026, 3:42 a.m. |
Created at: April 10, 2026, 1:16 a.m.