Triple
T15999572
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | VMU |
E388060
|
entity |
| Predicate | fullName |
P16
|
FINISHED |
| Object | Visual Memory Unit |
E388061
|
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: Visual Memory Unit | Statement: [VMU, fullName, Visual Memory Unit]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Visual Memory Unit Context triple: [VMU, fullName, Visual Memory Unit]
-
A.
Visual Memory Unit
chosen
The Visual Memory Unit is a specialized memory card for the Sega Dreamcast that doubles as a tiny handheld device with its own screen, controls, and mini-games.
-
B.
“A Semantic Model for Memory”
“A Semantic Model for Memory” is a foundational work in cognitive science and artificial intelligence that proposes how human memory can be represented and processed using structured semantic relationships.
-
C.
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information
Vision: A Computational Investigation into the Human Representation and Processing of Visual Information is a seminal 1982 book by David Marr that laid the foundations of computational neuroscience and modern theories of visual perception.
-
D.
Learning to See
"Learning to See" is an autobiographical essay by Eudora Welty that reflects on how her early experiences and observations shaped her development as a writer.
-
E.
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
"Long-term Recurrent Convolutional Networks for Visual Recognition and Description" is a research paper that introduces a deep learning architecture combining convolutional and recurrent neural networks to perform tasks like video recognition and automatic image or video captioning.
- 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_69d86daa562c81908aacc179c0fe8fb5 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1578a0adc819097c6a23514182173 |
completed | April 16, 2026, 9:41 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffc3d99ca08190a3d07a0802b1b24a |
completed | May 9, 2026, 11:31 p.m. |
Created at: April 10, 2026, 4:55 a.m.