Triple
T15974316
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | CT colonography |
E387404
|
entity |
| Predicate | imageDimension |
P83576
|
FINISHED |
| Object | 2D |
—
|
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: 2D | Statement: [CT colonography, imageDimension, 2D]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageDimension Context triple: [CT colonography, imageDimension, 2D]
-
A.
graphicsDimension
chosen
Indicates a relationship where an entity has or is associated with a specific graphical size or dimensional properties.
-
B.
mainDimension
Indicates the primary or most significant dimension by which something is measured, organized, or characterized within a given context.
-
C.
dimensionOfCurrent
Indicates the dimensional property (such as magnitude or units) associated with the current in a given context.
-
D.
mediaAspect
Indicates the specific aspect ratio or dimensional proportion of a media item in relation to its width and height.
-
E.
imageQuality
Indicates the assessed level or degree of visual clarity, detail, and overall fidelity of an 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e173b3bf6c81909230170e833d7ce7 |
completed | April 16, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69e142d6fb588190b4176eab4bbae774 |
completed | April 16, 2026, 8:13 p.m. |
Created at: April 10, 2026, 4:54 a.m.