Triple
T37049053
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Murang’a |
E916999
|
entity |
| Predicate | surroundedByRegionKnownFor |
P120961
|
FINISHED |
| Object | tea farming |
—
|
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: tea farming | Statement: [Murang’a, surroundedByRegionKnownFor, tea farming]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: surroundedByRegionKnownFor Context triple: [Murang’a, surroundedByRegionKnownFor, tea farming]
-
A.
nearbyRegionCharacterizedBy
Indicates that a region located nearby another entity is defined or distinguished by a particular characteristic, feature, or condition.
-
B.
surroundedAreaColloquialName
Indicates that an area is commonly referred to by a particular informal or colloquial name by people in the surrounding region.
-
C.
isLocatedInDistrictKnownFor
chosen
Indicates that an entity is situated within a district that is notably recognized for a particular characteristic, feature, or reputation.
-
D.
mentionsRegion
Indicates that one entity explicitly refers to or cites a specific geographic region in its content or context.
-
E.
surrounds
Indicates that one entity is located all around another entity, enclosing or encircling it on multiple sides or completely.
- 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_69f76e94d0308190a3f06890e133c88e |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb154c0fe08190a2e41e7a29b6055f |
completed | May 6, 2026, 10:17 a.m. |
| PD | Predicate disambiguation | batch_69f9fecc005c8190be082a8689193745 |
completed | May 5, 2026, 2:29 p.m. |
Created at: May 3, 2026, 4:14 p.m.