Triple
T5825671
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mfecane |
E129216
|
entity |
| Predicate | hasKeyFigure |
P810
|
FINISHED |
| Object | Zwide 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: Zwide kaLanga | Statement: [Mfecane, hasKeyFigure, Zwide kaLanga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zwide kaLanga Context triple: [Mfecane, hasKeyFigure, Zwide kaLanga]
-
A.
Zindziswa
Zindziswa is the given first name of Zindzi Mandela, the South African diplomat, poet, and daughter of Nelson Mandela and Winnie Madikizela-Mandela.
-
B.
Tazzelenghe
Tazzelenghe is a rare, intensely tannic red wine grape native to Italy’s Friuli region, known for producing deeply colored, robust wines with pronounced acidity and dark fruit flavors.
-
C.
Nodwengu
Nodwengu was a principal royal residence and political center of the Zulu Kingdom during the 19th century.
-
D.
Langa
Langa is a surname and place name found in various cultures, notably in Southern Africa and parts of Europe.
-
E.
Kalanga
chosen
Kalanga is a Southern Bantu language spoken primarily in southwestern Zimbabwe and northeastern Botswana by the Kalanga people.
- 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_69c00849d55481908b4f9f5543e0bf6d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0341a85988190be988f1c0722da66 |
completed | March 22, 2026, 6:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0985e3e748190abcc226abdcdb4b5 |
completed | March 23, 2026, 1:33 a.m. |
Created at: March 22, 2026, 3:53 p.m.