Triple
T17167602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erica |
E416645
|
entity |
| Predicate | distribution |
P1356
|
FINISHED |
| Object | South Africa |
E3669
|
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: South Africa | Statement: [Erica, distribution, South Africa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: South Africa Context triple: [Erica, distribution, South Africa]
-
A.
South Africa
chosen
South Africa is a country at the southern tip of the African continent, known for its cultural and linguistic diversity, complex history of apartheid and democratic transition, and significant economic and political influence in the region.
-
B.
Transkei, South Africa
Transkei, South Africa was a former bantustan in the southeastern part of the country, historically designated for Xhosa-speaking people during the apartheid era.
-
C.
Jong Suid-Afrika
Jong Suid-Afrika was the original name of the Afrikaner Broederbond, a secretive and influential Afrikaner nationalist organization in South Africa.
-
D.
Kwaluseni
Kwaluseni is a town in Eswatini known primarily as the main campus site of the University of Eswatini.
-
E.
Tiszanána
Tiszanána is a village in northern Hungary known for its proximity to the Tisza River and recreational areas around Lake Tisza.
- 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_69d886d5f34c8190b24564dfaa63f3fb |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3f916833c81909c64e0d74b40a85b |
completed | April 18, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01483d754c819089607cfc87d08d42 |
completed | May 11, 2026, 3:08 a.m. |
Created at: April 10, 2026, 5:37 a.m.