Triple
T3000925
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kenya |
E81182
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object |
Meru
Meru is a town in eastern Kenya that serves as a commercial and administrative hub for the surrounding agricultural region near Mount Kenya.
|
E318870
|
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: Meru | Statement: [Mount Kenya, nearCity, Meru]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meru Context triple: [Mount Kenya, nearCity, Meru]
-
A.
Mount Meru
Mount Meru is a dormant stratovolcano in northern Tanzania, renowned as one of Africa’s highest peaks and a prominent feature near Arusha and Mount Kilimanjaro.
-
B.
Gigiri
Gigiri is an affluent diplomatic and residential district in Nairobi, Kenya, known for hosting major international institutions and embassies.
-
C.
Mount Carlo
Mount Carlo is a lesser-known peak in the rugged Mahoosuc Range of the northern Appalachian Mountains in New England.
-
D.
Tanggula Mountains
The Tanggula Mountains are a high, remote mountain range on the Tibetan Plateau in China, known for forming part of the watershed between the Yangtze and other major Asian rivers.
-
E.
Illimani
Illimani is a towering, snow-capped mountain in the Bolivian Andes that serves as an iconic natural landmark visible from the city of La Paz.
- 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: Meru Triple: [Mount Kenya, nearCity, Meru]
Generated description
Meru is a town in eastern Kenya that serves as a commercial and administrative hub for the surrounding agricultural region near Mount Kenya.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Meru Target entity description: Meru is a town in eastern Kenya that serves as a commercial and administrative hub for the surrounding agricultural region near Mount Kenya.
-
A.
Mount Meru
Mount Meru is a dormant stratovolcano in northern Tanzania, renowned as one of Africa’s highest peaks and a prominent feature near Arusha and Mount Kilimanjaro.
-
B.
Gigiri
Gigiri is an affluent diplomatic and residential district in Nairobi, Kenya, known for hosting major international institutions and embassies.
-
C.
Mount Carlo
Mount Carlo is a lesser-known peak in the rugged Mahoosuc Range of the northern Appalachian Mountains in New England.
-
D.
Tanggula Mountains
The Tanggula Mountains are a high, remote mountain range on the Tibetan Plateau in China, known for forming part of the watershed between the Yangtze and other major Asian rivers.
-
E.
Illimani
Illimani is a towering, snow-capped mountain in the Bolivian Andes that serves as an iconic natural landmark visible from the city of La Paz.
- 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.