Triple
T1556361
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Banda languages |
E33212
|
entity |
| Predicate | arealClassification |
P26631
|
FINISHED |
| Object | Central Sudanic region |
—
|
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: Central Sudanic region | Statement: [Central Banda languages, arealClassification, Central Sudanic region]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: arealClassification Context triple: [Central Banda languages, arealClassification, Central Sudanic region]
-
A.
arealRegion
Indicates that something occupies or pertains to a specific two-dimensional geographic or spatial area.
-
B.
arealFeature
Indicates a relationship where something is characterized as a spatial or geographic feature occupying an area on a surface or map.
-
C.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
D.
urbanAreaType
Indicates the classification of an area based on its urban characteristics or development type (e.g., city, town, suburb, metropolitan region).
-
E.
regionClassification
chosen
Indicates how a given area or location is categorized into a specific region based on defined criteria or boundaries.
- 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_69a885ef9cf48190b0af0f5ce3d02231 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a9407d9d1481909597af97b16512cc |
completed | March 5, 2026, 8:36 a.m. |
| PD | Predicate disambiguation | batch_69a907b688d081908171f89010c53973 |
completed | March 5, 2026, 4:33 a.m. |
Created at: March 4, 2026, 7:27 p.m.