Triple
T3000924
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kenya |
E81182
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object |
Embu
Embu is a town in central Kenya that serves as a commercial and administrative hub on the southeastern slopes of Mount Kenya.
|
E318869
|
NE FINISHED |
How this triple was built (4 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: Embu | Statement: [Mount Kenya, nearCity, Embu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Embu Context triple: [Mount Kenya, nearCity, Embu]
-
A.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
B.
Corumbá
Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
-
C.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
D.
Itapira
Itapira is a municipality in southeastern Brazil known for its agricultural activities and location within the interior of the state of São Paulo.
-
E.
Combarbalá
Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Embu Triple: [Mount Kenya, nearCity, Embu]
Generated description
Embu is a town in central Kenya that serves as a commercial and administrative hub on the southeastern slopes of Mount Kenya.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Embu Target entity description: Embu is a town in central Kenya that serves as a commercial and administrative hub on the southeastern slopes of Mount Kenya.
-
A.
Ciluba
Ciluba is a Bantu language spoken primarily in the Democratic Republic of the Congo, especially in the Kasai region.
-
B.
Corumbá
Corumbá is a Brazilian city in the state of Mato Grosso do Sul, known as a key gateway to the Pantanal wetlands and an important regional center for river trade and ecotourism.
-
C.
Itatiba
Itatiba is a municipality in southeastern Brazil known for its quality of life and proximity to the metropolitan region of Campinas in the state of São Paulo.
-
D.
Itapira
Itapira is a municipality in southeastern Brazil known for its agricultural activities and location within the interior of the state of São Paulo.
-
E.
Combarbalá
Combarbalá is a small Chilean town and municipality in the Coquimbo Region, known for its semi-arid landscapes, goat farming, and distinctive combarbalite stone crafts.
- F. None of above. chosen
Provenance (5 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a1022e48190afee77db94635ff2 |
completed | March 8, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b12e4b54188190bf900bf10061a57a |
completed | March 11, 2026, 8:56 a.m. |
| NEDg | Description generation | batch_69b12f188c7c81908d1d575252dc4bda |
completed | March 11, 2026, 9 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b1c9bccb3081909e6869b5cba68117 |
completed | March 11, 2026, 7:59 p.m. |
Created at: March 8, 2026, 2:59 p.m.