Triple
T2732179
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hausa-Fulani |
E60338
|
entity |
| Predicate | typicalSettlementType |
P14278
|
FINISHED |
| Object | urban centers |
—
|
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: urban centers | Statement: [Hausa-Fulani, typicalSettlementType, urban centers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalSettlementType Context triple: [Hausa-Fulani, typicalSettlementType, urban centers]
-
A.
settlementType
Indicates the specific kind or category of human settlement an entity represents, such as a city, village, town, or hamlet.
-
B.
humanSettlementType
chosen
Indicates the classification of a human settlement based on its form or function, such as village, town, or city.
-
C.
mainSettlement
Indicates that one settlement serves as the primary or most important settlement associated with a given area, region, or administrative unit.
-
D.
typeOfSettlement
Indicates the specific category or classification of a settlement (e.g., city, town, village) that characterizes what kind of settlement it is.
-
E.
typicalSettlementCountry
Indicates the country in which an entity’s financial transactions, obligations, or trades are most commonly settled.
- 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_69ab4b75cd908190b691ef0d1801acda |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdaf011548190beb9c3feee7b743f |
completed | March 7, 2026, 7:59 a.m. |
| PD | Predicate disambiguation | batch_69abd82859348190bce3be8f2e9d60ba |
completed | March 7, 2026, 7:47 a.m. |
Created at: March 6, 2026, 9:56 p.m.