Triple
T13052494
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Coat of arms of Uganda |
E327480
|
entity |
| Predicate | greenMoundSymbolizes |
P108471
|
FINISHED |
| Object | fertile land |
—
|
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: fertile land | Statement: [Coat of arms of Uganda, greenMoundSymbolizes, fertile land]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: greenMoundSymbolizes Context triple: [Coat of arms of Uganda, greenMoundSymbolizes, fertile land]
-
A.
greenFieldSymbolizes
Indicates that a green field is used as a symbol representing or conveying a particular idea, quality, or concept.
-
B.
moundColor
Indicates the color associated with a mound.
-
C.
symbolicMountain
Indicates that a mountain functions as a symbol or emblem representing some idea, value, or entity.
-
D.
treeSymbolism
Indicates the use of a tree as a symbolic representation of an idea, quality, or relationship between entities.
-
E.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
- 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_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. |
| PDg | Predicate description generation | batch_69d98a9577d081908ddef9ea77e408e2 |
completed | April 10, 2026, 11:41 p.m. |
Created at: April 9, 2026, 8:57 p.m.