Triple
T34871093
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | British Empire in Kenya region |
E1005753
|
entity |
| Predicate | createdLandCategory |
P42853
|
FINISHED |
| Object | White Highlands |
—
|
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: White Highlands | Statement: [British Empire in Kenya region, createdLandCategory, White Highlands]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: createdLandCategory Context triple: [British Empire in Kenya region, createdLandCategory, White Highlands]
-
A.
createsCategory
Indicates that one entity establishes or brings into existence a new category for organizing or classifying other entities.
-
B.
mainLandmarkCreated
Indicates that one entity is the primary landmark that was created or established by another entity.
-
C.
usedToCreateLand
Indicates that something served as a means, material, or method for producing or forming a particular piece of land.
-
D.
createdFeature
chosen
Indicates that one entity brought another entity into existence or caused it to be produced as a feature.
-
E.
createdEntityType
Indicates the type or category of entity that was produced or brought into existence as a result of the creation action.
- 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_69f76dbde1c08190a24e7f9beb564c8d |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69ff5f5ecc808190b2df364da108ff4c |
completed | May 9, 2026, 4:22 p.m. |
| PD | Predicate disambiguation | batch_69ff5b84131c8190bf81d7fb53e934bc |
completed | May 9, 2026, 4:06 p.m. |
Created at: May 3, 2026, 4 p.m.