Triple
T23253300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Malamulele |
E581794
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Giyani |
—
|
NE NERFINISHED |
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: Giyani | Statement: [Malamulele, near, Giyani]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Giyani Context triple: [Malamulele, near, Giyani]
-
A.
Giyani
chosen
Giyani is a town in northeastern Limpopo, South Africa, known as an administrative and commercial center for the surrounding rural region.
-
B.
Mogoditshane
Mogoditshane is a rapidly growing suburban township located just outside Botswana’s capital, Gaborone.
-
C.
Buhe
Buhe was a Chinese politician of Mongol ethnicity who served in various regional leadership roles, including as chairman of the Inner Mongolia Autonomous Region.
-
D.
Makhado
Makhado is a major town in South Africa’s Limpopo province, serving as an important commercial and administrative center in the Vhembe region.
-
E.
Tzaneen
Tzaneen is a large agricultural town in South Africa’s Limpopo province, known for its subtropical climate and extensive fruit farming.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24606b17c81908aba1a4911c8a8ba |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f193f840dc819098e52272abefe616 |
completed | April 29, 2026, 5:15 a.m. |
Created at: April 17, 2026, 4:11 p.m.