Triple
T13052498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Uganda |
E327480
|
entity |
| Predicate | cottonSymbolizes |
P129
|
FINISHED |
| Object | cash crops |
—
|
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: cash crops | Statement: [Coat of arms of Uganda, cottonSymbolizes, cash crops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cottonSymbolizes Context triple: [Coat of arms of Uganda, cottonSymbolizes, cash crops]
-
A.
clothingSymbolism
Indicates how clothing or attire conveys symbolic meaning, such as status, identity, emotion, or cultural significance, within a given context.
-
B.
symbolismIn
Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
-
C.
symbolizes
chosen
Indicates that one entity stands for, represents, or is used as a sign for another entity, concept, or idea.
-
D.
reconstructionSymbolizes
Indicates that a reconstruction serves as a symbolic representation or stand-in for something else, such as an original object, event, or concept.
-
E.
typicalMaterialSymbolism
Indicates that a material is commonly or characteristically used to symbolize or represent something in a given cultural or contextual setting.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d98a9829b48190b23624b6b3df4600 |
completed | April 10, 2026, 11:41 p.m. |
| PD | Predicate disambiguation | batch_69d9803aca4c8190b1015cd159cc47a9 |
completed | April 10, 2026, 10:56 p.m. |
Created at: April 9, 2026, 8:57 p.m.