Triple
T25447256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Nations |
E637668
|
entity |
| Predicate | hasCountryOfficeIn |
P9395
|
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: [United Nations, hasCountryOfficeIn, Kenya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCountryOfficeIn Context triple: [United Nations, hasCountryOfficeIn, Kenya]
-
A.
hasAssociatedOffice
Indicates that an entity is linked to or connected with a particular office in an official or functional capacity.
-
B.
hasInternationalOrganizationOffice
chosen
Indicates that an international organization maintains an official office or physical presence at a given location.
-
C.
hasBandOfficeOf
Indicates that a specific band office serves as the administrative office for a particular band or band-related organization.
-
D.
countryOfOffice
Indicates the country in which a person’s official position, role, or office is held or exercised.
-
E.
hasCountry
Indicates that one entity possesses, is associated with, or is located within a specific 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_69e75db7c5048190b8da9cd7eeedb610 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f7979a073881909a4fde2558e6b6f3 |
completed | May 3, 2026, 6:44 p.m. |
| PD | Predicate disambiguation | batch_69f7961550f88190b7bb8a9155458b54 |
completed | May 3, 2026, 6:38 p.m. |
Created at: April 21, 2026, 2:02 p.m.