Triple
T9625039
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chieveley, Natal, South Africa |
E232438
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Colenso |
E808673
|
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: Colenso | Statement: [Chieveley, Natal, South Africa, near, Colenso]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Colenso Context triple: [Chieveley, Natal, South Africa, near, Colenso]
-
A.
Colenso
chosen
Colenso is a small town in South Africa’s KwaZulu-Natal province, historically known for its role in the Anglo-Zulu and Anglo-Boer wars.
-
B.
Calvinia
Calvinia is a small South African town in the Northern Cape, known for its sheep farming, clear night skies, and location within the arid Karoo region.
-
C.
Clanwilliam
Clanwilliam is a historic town in South Africa’s Western Cape, known as a gateway to the Cederberg mountains and for its rooibos tea production.
-
D.
Teralba
Teralba is a suburb of Lake Macquarie in New South Wales, Australia, known for its lakeside setting and historical ties to coal mining and rail transport.
-
E.
Lanseria
Lanseria is a town in the northwestern part of Johannesburg, South Africa, known primarily for hosting the privately owned Lanseria International Airport.
- 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_69ca848793ec8190a93a12383a754dc0 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9afb67c88190aa170716f0033752 |
completed | April 1, 2026, 10:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1797ab3d4819088da04fd5d00386b |
completed | April 4, 2026, 8:50 p.m. |
Created at: March 30, 2026, 8:10 p.m.