Triple
T29116100
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Elgon National Park (Kenya) |
E737048
|
entity |
| Predicate | borderingCountryAcrossMountain |
P156790
|
FINISHED |
| Object | Uganda |
—
|
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: Uganda | Statement: [Mount Elgon National Park (Kenya), borderingCountryAcrossMountain, Uganda]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderingCountryAcrossMountain Context triple: [Mount Elgon National Park (Kenya), borderingCountryAcrossMountain, Uganda]
-
A.
borderMountainOf
Indicates that a mountain lies along or forms part of the border of a specified region or entity.
-
B.
crossesMountainRangeTo
Indicates that one entity traverses from one side of a mountain range to the other, passing across or through it as a route or connection.
-
C.
crossesMountainRange
Indicates that one entity traverses from one side of a mountain range to the other, passing through or over it.
-
D.
borderedByCountryAcrossRiver
Indicates that one country shares a border with another country, with the boundary specifically formed or separated by a river.
-
E.
borderingCountryOnOtherSide
chosen
Indicates that one country lies on the opposite side of a shared border relative to another country.
- 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_69f077ed54e08190bb02a744e8121a66 |
completed | April 28, 2026, 9:03 a.m. |
| NER | Named-entity recognition | batch_69fd82ed2a4c81908bd7797fbd2e3d08 |
completed | May 8, 2026, 6:30 a.m. |
| PD | Predicate disambiguation | batch_69fd814cc10481908e4f8123d35a5d0c |
completed | May 8, 2026, 6:23 a.m. |
Created at: April 28, 2026, 11:22 a.m.