Triple
T6731194
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Korekore |
E153636
|
entity |
| Predicate | closelyRelatedTo |
P37
|
FINISHED |
| Object | Karanga |
E176053
|
NE 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: Karanga | Statement: [Korekore, closelyRelatedTo, Karanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karanga Context triple: [Korekore, closelyRelatedTo, Karanga]
-
A.
Karanga
chosen
Karanga is a major dialect of the Shona language spoken primarily in southern Zimbabwe, known for its distinct phonological and lexical features.
-
B.
Tangale
Tangale is a West Chadic language spoken primarily in Gombe State, northeastern Nigeria, by the Tangale people.
-
C.
Ronga
Ronga is a Bantu language spoken primarily in southern Mozambique, known for contributing vocabulary and structural features to African varieties of Portuguese.
-
D.
Urunga
Urunga is a small coastal town in New South Wales, Australia, known for its scenic boardwalks, estuary views, and relaxed seaside atmosphere.
-
E.
Mungaka
Mungaka is a Grassfields Bantu language spoken primarily in Cameroon, particularly associated with the Bamunka (Ndop) area.
- 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_69c6880bdd68819097de8b6099992682 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d16a30888190ae474d90bb71ac49 |
completed | March 27, 2026, 6:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7129f2d2c81908102eabbae3935d7 |
completed | March 27, 2026, 11:28 p.m. |
Created at: March 27, 2026, 2:09 p.m.