Triple
T29935495
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kiboga District |
E760347
|
entity |
| Predicate | hasPredominantAreaType |
P6822
|
FINISHED |
| Object | Rural |
—
|
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: Rural | Statement: [Kiboga District, hasPredominantAreaType, Rural]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPredominantAreaType Context triple: [Kiboga District, hasPredominantAreaType, Rural]
-
A.
hasAreaType
chosen
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
isOneOfMainAreasFor
Indicates that something belongs to the primary set of domains, topics, or fields associated with another entity.
-
C.
hasPrimaryServiceArea
Indicates that an entity is associated with a main geographic or functional area in which it primarily provides its services.
-
D.
hasMetropolitanAreaType
Indicates that an entity is associated with a specific type or classification of metropolitan area (e.g., urban, suburban, metropolitan region category).
-
E.
appliesToUrbanAreaType
Indicates that something (such as a rule, measure, or classification) is applicable specifically to a particular type or category of urban area.
- 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_69f22463f3648190a603c3ff305c660b |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69ffacdf9f5c8190baef0245edfe87fc |
completed | May 9, 2026, 9:53 p.m. |
| PD | Predicate disambiguation | batch_69ffac5e86e08190a1e6da0840a237ad |
completed | May 9, 2026, 9:51 p.m. |
Created at: April 29, 2026, 6:20 p.m.