Triple
T34144787
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toposa people |
E875818
|
entity |
| Predicate | borderingCountryInteraction |
P199682
|
FINISHED |
| Object | Kenya |
—
|
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: Kenya | Statement: [Toposa people, borderingCountryInteraction, Kenya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: borderingCountryInteraction Context triple: [Toposa people, borderingCountryInteraction, Kenya]
-
A.
sharesInternationalBorderWith
Indicates that two geographic or political entities have a common boundary that is recognized as an international border.
-
B.
borderingCountryContext
Indicates that one country shares a land or maritime boundary with another within a specified geopolitical or temporal context.
-
C.
borderingCountryConnectivity
Indicates that two countries share a land or maritime border that allows direct movement or interaction between them.
-
D.
borderingRepublic
Indicates that one republic shares a land or maritime border with another republic.
-
E.
hasCountryBorderWith
Indicates that two countries share a common land or maritime boundary.
- F. None of above. chosen
Provenance (4 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_69f349abaa508190a820f206620efddc |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ff4fc6077c8190b8fd9b43fcfde986 |
completed | May 9, 2026, 3:16 p.m. |
| PD | Predicate disambiguation | batch_69ff4e61fb648190a72f7918961ece9c |
completed | May 9, 2026, 3:10 p.m. |
| PDg | Predicate description generation | batch_69ff4fc5289c819084abd5ede185e96b |
completed | May 9, 2026, 3:16 p.m. |
Created at: May 1, 2026, 1:54 a.m.