Triple
T1187817
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Southern Bantu |
E25286
|
entity |
| Predicate | hasMemberLanguage |
P7390
|
FINISHED |
| Object | Venda |
E52951
|
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: Venda | Statement: [Southern Bantu, hasMemberLanguage, Venda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Venda Context triple: [Southern Bantu, hasMemberLanguage, Venda]
-
A.
Venda
chosen
Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
-
B.
Sellin
Sellin is a seaside resort town on the German island of Rügen, known for its historic pier and Baltic Sea beaches.
-
C.
Solan
Solan is a town in the Indian state of Himachal Pradesh known for its mushroom cultivation and as a growing commercial and educational hub in the region.
-
D.
Natal
Natal is a historical region in southeastern South Africa, centered on the port city of Durban and known for its colonial history and diverse cultural heritage.
-
E.
Natal
Natal is a coastal city in northeastern Brazil known for its beaches, sand dunes, and role as a regional tourism and economic hub.
- 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_69a49427d98881908646d6c63b8cea1e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd568cf481908d10cf19a3ce28f3 |
completed | March 1, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac764a5f508190a54c8f01cf0b0d11 |
completed | March 7, 2026, 7:02 p.m. |
Created at: March 1, 2026, 7:45 p.m.