Triple
T4429195
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mbabane |
E95282
|
entity |
| Predicate | currency |
P245
|
FINISHED |
| Object | lilangeni |
E103007
|
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: lilangeni | Statement: [Mbabane, currency, lilangeni]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: lilangeni Context triple: [Mbabane, currency, lilangeni]
-
A.
Lilangeni
chosen
The lilangeni is the official monetary unit of Eswatini, subdivided into 100 cents and commonly used alongside the South African rand.
-
B.
Rolihlahla
Rolihlahla is the Xhosa birth name of Nelson Mandela, meaning “troublemaker” and reflecting his cultural origins.
-
C.
Mabalako
Mabalako is a health zone in North Kivu Province in the eastern Democratic Republic of the Congo, known for being heavily affected by Ebola outbreaks.
-
D.
Maphelane
Maphelane is a coastal nature reserve in South Africa known for its high vegetated dunes, rich birdlife, and diverse estuarine and marine habitats within the iSimangaliso Wetland Park.
-
E.
Mtiuleti
Mtiuleti is a mountainous historical region in northeastern Georgia known for its rugged landscapes and traditional highland villages.
- 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35568767c819084d5e18b56a4745e |
completed | March 13, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6136caa248190a84423cede1908c3 |
completed | March 15, 2026, 2:03 a.m. |
Created at: March 12, 2026, 11:30 p.m.