Triple
T2446368
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kikuyu |
E53602
|
entity |
| Predicate | majorLanguageOf |
P11430
|
FINISHED |
| Object | central Kenya |
—
|
LITERAL 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: central Kenya | Statement: [Kikuyu, majorLanguageOf, central Kenya]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: majorLanguageOf Context triple: [Kikuyu, majorLanguageOf, central Kenya]
-
A.
majorityLanguageOf
chosen
Indicates that a given language is the primary or most widely spoken language within a specified group, region, or entity.
-
B.
macrolanguageOf
Indicates that one language functions as a macrolanguage encompassing or grouping together one or more related individual languages.
-
C.
majorLanguageGroupOf
Indicates that one language group is the primary or dominant linguistic classification to which another language or set of languages belongs.
-
D.
standardLanguageOf
Indicates that one entity serves as the officially recognized or commonly used standard language for another entity (such as a country, region, or organization).
-
E.
languageFamilyDominant
Indicates that one language family holds a primary or prevailing status over others within a given context (such as a region, population, or system).
- 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_69ab495d227c8190b26ae6548eeb1019 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abcabf477081908512df63ac1a6414 |
completed | March 7, 2026, 6:50 a.m. |
| PD | Predicate disambiguation | batch_69abc5ad8e588190b97c4cd7cf575043 |
completed | March 7, 2026, 6:29 a.m. |
Created at: March 6, 2026, 9:43 p.m.