Triple
T16088060
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Saint Vincent and the Grenadines |
E390283
|
entity |
| Predicate | femaleFigure2Attribute |
P121852
|
FINISHED |
| Object | scales of justice |
—
|
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: scales of justice | Statement: [Coat of arms of Saint Vincent and the Grenadines, femaleFigure2Attribute, scales of justice]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: femaleFigure2Attribute Context triple: [Coat of arms of Saint Vincent and the Grenadines, femaleFigure2Attribute, scales of justice]
-
A.
femaleFeature
Indicates that the subject possesses a characteristic or attribute that is typically associated with females.
-
B.
fashionCharacteristic
Indicates a relationship where one entity possesses or exhibits a particular style, trend, or fashion-related attribute in relation to another.
-
C.
femaleSubject
Indicates that the subject in the relationship or action is female.
-
D.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
E.
legCharacteristic
Indicates a characteristic, property, or attribute that specifically pertains to the legs of an entity.
- 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_69d87f198bc48190a8b7e53ca15b7ead |
completed | April 10, 2026, 4:39 a.m. |
| NER | Named-entity recognition | batch_69e1ff63edb0819092cbb671967bbdcd |
completed | April 17, 2026, 9:37 a.m. |
| PD | Predicate disambiguation | batch_69e1827ad7c88190b867da511cbfb7fa |
completed | April 17, 2026, 12:44 a.m. |
| PDg | Predicate description generation | batch_69e1ff5cd7e481908a29214139a3de2e |
completed | April 17, 2026, 9:37 a.m. |
Created at: April 10, 2026, 4:59 a.m.