Triple
T6105625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mangaung Metropolitan Municipality |
E136109
|
entity |
| Predicate | includesUrbanAreas |
P11388
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Mangaung Metropolitan Municipality, includesUrbanAreas, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: includesUrbanAreas Context triple: [Mangaung Metropolitan Municipality, includesUrbanAreas, yes]
-
A.
containsUrbanArea
chosen
Indicates that a geographic region fully or partially encompasses an urbanized area within its boundaries.
-
B.
formsUrbanAreaWith
Indicates that two or more settlements are geographically and functionally connected so that together they constitute a single continuous urban area.
-
C.
withinUrbanArea
Indicates that one entity is located inside the spatial boundaries of an urban area associated with another entity.
-
D.
statusInUrbanAreas
Indicates the condition, prevalence, or situation of something specifically within urban areas.
-
E.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
- 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_69c0087dee9881909e3655be88208c01 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c05b7ee8b48190b87f5ec8a46d6e2d |
completed | March 22, 2026, 9:13 p.m. |
| PD | Predicate disambiguation | batch_69c049f80e2081909b7d84a104cda68d |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:13 p.m.