Triple
T9740094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | REYES architecture |
E236164
|
entity |
| Predicate | micropolygonSize |
P37041
|
FINISHED |
| Object | approximately one pixel or smaller |
—
|
LITERAL 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: approximately one pixel or smaller | Statement: [REYES architecture, micropolygonSize, approximately one pixel or smaller]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: micropolygonSize Context triple: [REYES architecture, micropolygonSize, approximately one pixel or smaller]
-
A.
pixelScale
chosen
Indicates the ratio or conversion factor between pixel units and real-world or coordinate-space units in a representation or image.
-
B.
cellSize
Indicates the physical dimensions or volume of a cell in a biological or computational context.
-
C.
grainSize
Indicates the relative coarseness or fineness of the material or particles involved in the relationship.
-
D.
minimumUnitSize
Indicates that there is a smallest allowable or defined size or quantity for the unit involved in the relationship.
-
E.
modelSize
Indicates the quantitative measure of how large or complex a model is, typically in terms of parameters, layers, or memory footprint.
- F. None of above.
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_69ca84d313e88190983ee6ffd0ef60d2 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9f29a5bc8190b2b391017405c71e |
completed | April 1, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69cd03cc128c81908b84ef224f858b4e |
completed | April 1, 2026, 11:38 a.m. |
Created at: March 30, 2026, 8:22 p.m.