Triple
T612174
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kilimanjaro |
E12121
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object |
Moshi
Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
|
E76525
|
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: Moshi | Statement: [Mount Kilimanjaro, nearCity, Moshi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Moshi Context triple: [Mount Kilimanjaro, nearCity, Moshi]
-
A.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
B.
Arusha, Tanzania
Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
-
C.
Nairobi
Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
-
D.
Kilwa Kisiwani
Kilwa Kisiwani is a historic Swahili coastal city-state in present-day Tanzania that flourished as a powerful center of Indian Ocean trade between Africa, Arabia, and Asia from the medieval period onward.
-
E.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
- 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: Moshi Triple: [Mount Kilimanjaro, nearCity, Moshi]
Generated description
Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Moshi Target entity description: Moshi is a Tanzanian town in the Kilimanjaro Region that serves as a major gateway and base for climbers ascending Mount Kilimanjaro.
-
A.
Mombasa
Mombasa is a major coastal city in Kenya known as a key regional port and historic trading hub on the Indian Ocean.
-
B.
Arusha, Tanzania
Arusha, Tanzania is a major city in northern Tanzania known as a diplomatic hub and gateway to popular safari destinations and Mount Kilimanjaro.
-
C.
Nairobi
Nairobi is the capital and largest city of Kenya, serving as a major political, economic, and cultural hub in East Africa.
-
D.
Kilwa Kisiwani
Kilwa Kisiwani is a historic Swahili coastal city-state in present-day Tanzania that flourished as a powerful center of Indian Ocean trade between Africa, Arabia, and Asia from the medieval period onward.
-
E.
Kisumu
Kisumu is a major Kenyan city on the shores of Lake Victoria, serving as a key commercial and transport hub in western Kenya.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e07739481909930a6577c081b9e |
completed | March 1, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a533dabe288190ab25bd6d76e79d06 |
completed | March 2, 2026, 6:53 a.m. |
| NEDg | Description generation | batch_69a54e4849f48190868d7b624e450dc3 |
completed | March 2, 2026, 8:46 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a55048e2ec81908d306f44b2ca24fa |
completed | March 2, 2026, 8:54 a.m. |
Created at: March 1, 2026, 7:35 p.m.