Triple
T418239
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gauteng |
E8041
|
entity |
| Predicate | officialLanguage |
P236
|
FINISHED |
| Object |
Venda
Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
|
E52951
|
NE FINISHED |
How this triple was built (4 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: [Gauteng, officialLanguage, Venda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Venda Context triple: [Gauteng, officialLanguage, Venda]
-
A.
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.
-
B.
Davidville
Davidville was the original company founded by David Karp that created and initially operated the microblogging platform Tumblr.
-
C.
Vallejo
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
D.
Larcomar
Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
-
E.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Venda Triple: [Gauteng, officialLanguage, Venda]
Generated description
Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Venda Target entity description: Venda is a Bantu language of the Venda people of South Africa and Zimbabwe, recognized as one of South Africa’s official languages.
-
A.
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.
-
B.
Davidville
Davidville was the original company founded by David Karp that created and initially operated the microblogging platform Tumblr.
-
C.
Vallejo
Vallejo is a waterfront city in the San Francisco Bay Area known for its former Mare Island Naval Shipyard and diverse, working-class community.
-
D.
Larcomar
Larcomar is a popular cliffside shopping and entertainment center in Lima, Peru, overlooking the Pacific Ocean and known for its restaurants, boutiques, and ocean views.
-
E.
Barra
Barra is a scenic island in the Outer Hebrides of Scotland, known for its rugged coastline, Gaelic culture, and the unique beach runway at Barra Airport.
- F. None of above. chosen
Provenance (5 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_69a2e7f1d1bc81909cf2dc9754a3c334 |
completed | Feb. 28, 2026, 1:04 p.m. |
| NER | Named-entity recognition | batch_69a2ee9059248190ba901680431914b5 |
completed | Feb. 28, 2026, 1:33 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a423a4debc819098e13855b550a72e |
completed | March 1, 2026, 11:31 a.m. |
| NEDg | Description generation | batch_69a42418a28c81909ee31dfb1819b87f |
completed | March 1, 2026, 11:33 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a42488226c81908c9c81567fed0efa |
completed | March 1, 2026, 11:35 a.m. |
Created at: Feb. 28, 2026, 1:11 p.m.