Triple
T612145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kilimanjaro |
E12121
|
entity |
| Predicate | hasVolcanicCone |
P6356
|
FINISHED |
| Object |
Mawenzi
Mawenzi is the jagged, eroded eastern peak of Mount Kilimanjaro and one of its three main volcanic cones.
|
E78468
|
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: Mawenzi | Statement: [Mount Kilimanjaro, hasVolcanicCone, Mawenzi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mawenzi Context triple: [Mount Kilimanjaro, hasVolcanicCone, Mawenzi]
-
A.
Nyanga Highlands
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
-
B.
Kiliwa
Kiliwa is an indigenous people of northern Baja California, Mexico, known for their distinct Yuman language and traditional hunter-gatherer culture.
-
C.
Mount Nyangani
Mount Nyangani is a prominent mountain in eastern Zimbabwe known for its scenic highland landscapes and status as a popular hiking destination.
-
D.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
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: Mawenzi Triple: [Mount Kilimanjaro, hasVolcanicCone, Mawenzi]
Generated description
Mawenzi is the jagged, eroded eastern peak of Mount Kilimanjaro and one of its three main volcanic cones.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mawenzi Target entity description: Mawenzi is the jagged, eroded eastern peak of Mount Kilimanjaro and one of its three main volcanic cones.
-
A.
Nyanga Highlands
Nyanga Highlands is a mountainous region in eastern Zimbabwe known for its scenic landscapes, cool climate, and popular hiking and holiday resorts.
-
B.
Kiliwa
Kiliwa is an indigenous people of northern Baja California, Mexico, known for their distinct Yuman language and traditional hunter-gatherer culture.
-
C.
Mount Nyangani
Mount Nyangani is a prominent mountain in eastern Zimbabwe known for its scenic highland landscapes and status as a popular hiking destination.
-
D.
Kigoma
Kigoma is a port city in western Tanzania located on the eastern shore of Lake Tanganyika and serving as a key regional transport and trade hub.
-
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_69a4a514b514819088e7b6b7e4675905 |
completed | March 1, 2026, 8:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a566ff095081909a897d3001955514 |
completed | March 2, 2026, 10:31 a.m. |
| NEDg | Description generation | batch_69a567fa633881909a18c4530c3342e8 |
completed | March 2, 2026, 10:35 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a5687af6f48190af5a5424ca7da9c8 |
completed | March 2, 2026, 10:37 a.m. |
Created at: March 1, 2026, 7:35 p.m.