Triple
T19891596
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Province, Zambia |
E478043
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object | Itezhi-Tezhi |
—
|
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: Itezhi-Tezhi | Statement: [Central Province, Zambia, hasTown, Itezhi-Tezhi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Itezhi-Tezhi Context triple: [Central Province, Zambia, hasTown, Itezhi-Tezhi]
-
A.
Itezhi-Tezhi
chosen
Itezhi-Tezhi is a town in Zambia known for its proximity to the Itezhi-Tezhi Dam and Kafue National Park.
-
B.
Wedza
Wedza is a rural district and township in northeastern Zimbabwe known for its agriculture and gold deposits.
-
C.
Kijitonyama
Kijitonyama is a residential and commercial neighborhood in Dar es Salaam, Tanzania, known as one of the urban wards within the Kinondoni District.
-
D.
Bafut
Bafut is a traditional kingdom and town in northwestern Cameroon known for its rich cultural heritage and historical palace.
-
E.
Oshikwanyama
Oshikwanyama is a Bantu language variety spoken primarily in northern Namibia and southern Angola, recognized as one of the major dialects of Oshiwambo.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6590ed7988190bc6b610d1f4fa194 |
completed | April 20, 2026, 4:49 p.m. |
Created at: April 10, 2026, 1:52 p.m.