Triple
T15218087
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | KMNIST |
E363690
|
entity |
| Predicate | complexityRelativeToMNIST |
P28756
|
FINISHED |
| Object | higher |
—
|
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: higher | Statement: [KMNIST, complexityRelativeToMNIST, higher]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: complexityRelativeToMNIST Context triple: [KMNIST, complexityRelativeToMNIST, higher]
-
A.
iconographicComplexity
Indicates the degree to which an image or visual representation contains multiple layers of symbols, motifs, and visual elements that contribute to its interpretive richness.
-
B.
hasComplexity
chosen
Indicates that something possesses a certain level or type of complexity, often in terms of structure, behavior, or difficulty.
-
C.
hasReasoningComplexity
Indicates that an action, process, or decision involves a certain level or type of cognitive or logical complexity in its reasoning.
-
D.
typicalComplexity
Indicates the usual or characteristic level of complexity associated with an entity, process, or situation.
-
E.
trainingCompute
Indicates the amount or configuration of computational resources used to train a model or system.
- 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_69d85a0ce24c81909c4d3b6475548c95 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076f90c481909989befe031a2cae |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deca8479188190b2e5d3bc708d7d07 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:11 a.m.