Triple
T16431827
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | WATSON camera |
E399085
|
entity |
| Predicate | imageScale |
P37041
|
FINISHED |
| Object | microscopic to millimeter scale features |
—
|
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: microscopic to millimeter scale features | Statement: [WATSON camera, imageScale, microscopic to millimeter scale features]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageScale Context triple: [WATSON camera, imageScale, microscopic to millimeter scale features]
-
A.
similarScaleTo
Indicates that two entities have comparable magnitude, size, or extent along a given dimension or measurement scale.
-
B.
scaleOptimizedFor
Indicates that something has been adjusted or configured to operate most efficiently at a particular size, level, or magnitude.
-
C.
userScale
Indicates that a user adjusts or sets the scale, size, or zoom level of an object, interface, or content.
-
D.
areaScale
Indicates a proportional relationship where one area value is a scaled (enlarged or reduced) version of another by a specific factor.
-
E.
pixelScale
chosen
Indicates the ratio or conversion factor between pixel units and real-world or coordinate-space units in a representation or image.
- 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_69d87f2b9024819085c20e52de95d583 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e32b9dffe48190a23852f828af55d8 |
completed | April 18, 2026, 6:58 a.m. |
| PD | Predicate disambiguation | batch_69e22701d2288190bf8676050758f172 |
completed | April 17, 2026, 12:26 p.m. |
Created at: April 10, 2026, 5:10 a.m.