Triple
T16978403
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zaka District |
E411874
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Bikita District |
E419537
|
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: Bikita District | Statement: [Zaka District, borderedBy, Bikita District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bikita District Context triple: [Zaka District, borderedBy, Bikita District]
-
A.
Bikita District
chosen
Bikita District is an administrative district in southeastern Zimbabwe known for its rural communities and significant lithium and other mineral deposits.
-
B.
Guichi District
Guichi District is an urban district that serves as the central administrative and commercial hub of Chizhou in Anhui Province, China.
-
C.
Busiki District
Busiki District is an administrative district located in the Eastern Region of Uganda, known for its predominantly rural communities and agriculture-based economy.
-
D.
Gakenke District
Gakenke District is an administrative district in northern Rwanda known for its hilly terrain, rural communities, and agricultural activities.
-
E.
Miyanosawa district
Miyanosawa district is a residential and commercial neighborhood located in Nishi-ku, Sapporo, Japan.
- 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_69d886ca8f348190812768ea8d5055ce |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d185a9408190a991bf8a1ef694f0 |
completed | April 18, 2026, 6:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a015fb99b348190a6db655cd8aee799 |
completed | May 11, 2026, 4:48 a.m. |
Created at: April 10, 2026, 5:32 a.m.