Triple
T14999083
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | flag of Tanzania |
E374035
|
entity |
| Predicate | colourMeaningGreen |
P38847
|
FINISHED |
| Object | agriculture and natural vegetation |
—
|
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: agriculture and natural vegetation | Statement: [flag of Tanzania, colourMeaningGreen, agriculture and natural vegetation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: colourMeaningGreen Context triple: [flag of Tanzania, colourMeaningGreen, agriculture and natural vegetation]
-
A.
starColorSymbolism
Indicates how the color of a star is associated with particular symbolic meanings or themes.
-
B.
greenRepresents
chosen
Indicates that one entity uses the color green to symbolize, denote, or stand for another entity or concept.
-
C.
primaryColorSymbolism
Indicates how a primary color is symbolically associated with particular meanings, emotions, or concepts in a given context.
-
D.
greenFieldSymbolizes
Indicates that a green field is used as a symbol representing or conveying a particular idea, quality, or concept.
-
E.
greenPrimary_y
Indicates that the referenced entity serves as the primary or dominant green component in a color representation or relationship.
- 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_69d85ccc84388190aa151e5173370c8d |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded71a5618819083ae96a79735ef98 |
completed | April 15, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69de9a6169b48190a679609febd2d0e3 |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:54 a.m.