Triple
T2775422
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cape Province |
E61556
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Knysna |
E58257
|
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: Knysna | Statement: [Cape Province, contains, Knysna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Knysna Context triple: [Cape Province, contains, Knysna]
-
A.
Knysna
chosen
Knysna is a picturesque coastal town in South Africa known for its lagoon, indigenous forests, and role as a major tourist destination along the Garden Route.
-
B.
Oudenhoorn
Oudenhoorn is a small village in the Dutch province of South Holland, known for its rural character and historic polder landscape.
-
C.
Mossel Bay
Mossel Bay is a coastal town and popular tourist destination on South Africa’s Garden Route, known for its beaches, mild climate, and maritime history.
-
D.
Plettenberg Bay
Plettenberg Bay is a popular coastal resort town on South Africa’s Garden Route, known for its beaches, marine life, and scenic surroundings.
-
E.
Hermanus
Hermanus is a coastal town in South Africa renowned for its land-based whale watching and scenic seaside landscapes.
- 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_69ab4b7cd13481909174bca9809ed259 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd81015481908785fbef0326a2db |
completed | March 7, 2026, 8:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b08639b9108190badd8d22aaf74544 |
completed | March 10, 2026, 8:59 p.m. |
Created at: March 6, 2026, 9:57 p.m.