Triple
T32909146
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Buchosa District |
E841824
|
entity |
| Predicate | partOfLakeRegion |
P76780
|
FINISHED |
| Object | Lake Victoria zone of Tanzania |
—
|
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: Lake Victoria zone of Tanzania | Statement: [Buchosa District, partOfLakeRegion, Lake Victoria zone of Tanzania]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: partOfLakeRegion Context triple: [Buchosa District, partOfLakeRegion, Lake Victoria zone of Tanzania]
-
A.
hasLakeRegion
Indicates that a place or geographic area includes or is associated with a specific lake region.
-
B.
locatedInLakeRegion
chosen
Indicates that the subject entity is situated within a geographic area characterized as a lake region.
-
C.
hasNearbyLakeRegion
Indicates that one region is located close to a lake or lake-dominated area.
-
D.
locatedBetweenLakes
Indicates that something is situated in the area separating two lakes, with each lake on a different side of it.
-
E.
lakeIsOneOf
Indicates that the subject lake belongs to a specified set or category of lakes.
- F. None of above.
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_69f34946a5208190bbd79f0fec4323bd |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69ffc605b0648190a7abe9128b0d857a |
completed | May 9, 2026, 11:40 p.m. |
| PD | Predicate disambiguation | batch_69ffc5742d80819099f947ece78d5700 |
completed | May 9, 2026, 11:38 p.m. |
Created at: May 1, 2026, 1:19 a.m.