Triple
T7128947
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kalanga people |
E166136
|
entity |
| Predicate | ethnonym |
P4709
|
FINISHED |
| Object | Kalanga |
E143249
|
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: Kalanga | Statement: [Kalanga people, ethnonym, Kalanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kalanga Context triple: [Kalanga people, ethnonym, Kalanga]
-
A.
Kalanga
chosen
Kalanga is a Southern Bantu language spoken primarily in southwestern Zimbabwe and northeastern Botswana by the Kalanga people.
-
B.
Talanga
Talanga is a town and municipality in central Honduras known for its agricultural activities and location along the highway connecting Tegucigalpa with the country's northern regions.
-
C.
Loenga
Loenga is a small residential and industrial neighborhood in Oslo, Norway, situated near the railway yards and the Oslofjord.
-
D.
Kabaena
Kabaena is an island in Indonesia known for its location off the coast of Sulawesi and its mix of coastal and hilly landscapes.
-
E.
Sanglechi
Sanglechi is a lesser-known Eastern Iranian language spoken in parts of northeastern Afghanistan and adjacent regions.
- 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_69c6888350588190870cd552b427a1cd |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e66c87848190b0ffd08e3c3f4877 |
completed | March 27, 2026, 8:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7a33bf244819096db1351ebf62413 |
completed | March 28, 2026, 9:45 a.m. |
Created at: March 27, 2026, 2:44 p.m.