Triple
T428271
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Western Cape |
E9657
|
entity |
| Predicate | largestCity |
P235
|
FINISHED |
| Object | Cape Town |
E24410
|
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: Cape Town | Statement: [Western Cape, largestCity, Cape Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cape Town Context triple: [Western Cape, largestCity, Cape Town]
-
A.
Cape Town
chosen
Cape Town is a major coastal city in South Africa known for its iconic Table Mountain, diverse culture, and role as the country’s legislative capital.
-
B.
Durban
Durban is a major coastal city in South Africa known for its busy port, subtropical climate, and significant Indian community.
-
C.
Port Elizabeth
Port Elizabeth is a large coastal city in South Africa known for its major seaport, automotive industry, and popular beaches along Algoa Bay.
-
D.
Johannesburg, South Africa
Johannesburg, South Africa is the country’s largest city and economic hub, known for its role in the gold mining industry and as a major urban center in Gauteng province.
-
E.
Bloemfontein
Bloemfontein is a major South African city known as the seat of the country’s highest courts and one of its three national capitals.
- 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_69a2e801e1d48190b505d1dd336b52ac |
completed | Feb. 28, 2026, 1:05 p.m. |
| NER | Named-entity recognition | batch_69a2eeecb64c81908c5c83ef7c0181e6 |
completed | Feb. 28, 2026, 1:34 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a49eb65d548190899347f23ccee2e5 |
completed | March 1, 2026, 8:16 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.