Triple
T18016584
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | RetinaNet |
E431010
|
entity |
| Predicate | accuracyCharacteristic |
P62233
|
FINISHED |
| Object | high detection accuracy |
—
|
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: high detection accuracy | Statement: [RetinaNet, accuracyCharacteristic, high detection accuracy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: accuracyCharacteristic Context triple: [RetinaNet, accuracyCharacteristic, high detection accuracy]
-
A.
hasAccuracy
chosen
Indicates that something possesses a specified level or measure of correctness, precision, or exactness in relation to a standard or reference.
-
B.
reliabilityCharacteristic
Indicates that one entity specifies or embodies a reliability-related property, feature, or performance attribute of another entity.
-
C.
accuracyDependsOn
Indicates that the accuracy of one entity or process is contingent upon, or influenced by, another entity or factor.
-
D.
valueCharacteristic
Indicates that one entity serves as a value or specific quantitative/qualitative measure that characterizes or describes another entity.
-
E.
dataCharacteristic
Indicates that one entity specifies a property, attribute, or feature that characterizes a given piece of data.
- 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_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. |
Created at: April 10, 2026, 10:24 a.m.