Triple
T18016586
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RetinaNet |
E431010
|
entity |
| Predicate | usesMultiScaleFeatures |
P129424
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [RetinaNet, usesMultiScaleFeatures, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: usesMultiScaleFeatures Context triple: [RetinaNet, usesMultiScaleFeatures, true]
-
A.
requiresFeatureScaling
Indicates that applying feature scaling is a necessary preprocessing step for the associated data or model.
-
B.
hasLargeScale
Indicates that an entity operates, exists, or is implemented at a large or extensive scale relative to typical or baseline cases.
-
C.
isScaleInvariant
Indicates that a property, equation, or system remains unchanged when all relevant dimensions or variables are uniformly scaled by a common factor.
-
D.
isFeatureBased
Indicates that one entity is derived from, determined by, or constructed using the characteristics or attributes of another entity.
-
E.
hasScales
Indicates that an entity possesses scales as a surface covering or body feature.
- F. None of above. chosen
Provenance (4 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b9be5d0c819097e006f32d98753a |
completed | April 19, 2026, 11:17 a.m. |
| PD | Predicate disambiguation | batch_69e3f904b8048190add43883cd7cb191 |
completed | April 18, 2026, 9:35 p.m. |
| PDg | Predicate description generation | batch_69e42d8eefa88190a700c7c1b4213e46 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:24 a.m.