Triple
T17364091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southwest Region of Cameroon |
E422142
|
entity |
| Predicate | majorCity |
P316
|
FINISHED |
| Object | Limbe |
—
|
NE ONNED1 |
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: Limbe | Statement: [Southwest Region of Cameroon, majorCity, Limbe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Limbe Context triple: [Southwest Region of Cameroon, majorCity, Limbe]
-
A.
Limbe
chosen
Limbe is a coastal city in southwestern Cameroon known for its black sand beaches, oil industry, and cultural diversity.
-
B.
Limbe
Limbe is a major commercial and industrial township within the Blantyre urban area in southern Malawi.
-
C.
Mvele
Mvele is a Bantu language spoken by the Beti-Pahuin people in parts of Central Africa, particularly in Cameroon.
-
D.
Chilomoni
Chilomoni is a residential suburb of Blantyre in southern Malawi, known for its dense population and local markets.
-
E.
Negombo
Negombo is a coastal city in western Sri Lanka known historically as a strategic colonial port and today for its fishing industry and beach tourism.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a4f52988190847230e119a35b87 |
completed | April 19, 2026, 2:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01956294ec819083c1dde8fc2685e4 |
in_progress | May 11, 2026, 8:37 a.m. |
Created at: April 10, 2026, 5:44 a.m.