Triple
T19227681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haruna Mountains |
E480781
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Mount Kamonga
Mount Kamonga is a peak within Japan’s Haruna Mountains, a volcanic range in Gunma Prefecture known for its scenic landscapes and outdoor recreation.
|
E1374628
|
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: Mount Kamonga | Statement: [Haruna Mountains, hasPart, Mount Kamonga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Kamonga Context triple: [Haruna Mountains, hasPart, Mount Kamonga]
-
A.
Mount Kanobili
Mount Kanobili is a mountain in southern Georgia notable for hosting the Abastumani Astrophysical Observatory.
-
B.
Mount Kaikoma
Mount Kaikoma is a prominent, granite-peaked mountain in Japan’s Southern Alps, known for its rugged terrain and popular hiking and climbing routes.
-
C.
Mount Heha
Mount Heha is the tallest mountain in Burundi, located in the Burundi Highlands near the city of Bujumbura.
-
D.
Mount Noro
Mount Noro is a scenic mountain in Kure, Japan, known for its panoramic views of the Seto Inland Sea and popular hiking and sightseeing opportunities.
-
E.
Mount Amba
Mount Amba is a hill in Kinshasa, Democratic Republic of the Congo, known primarily as the site of the University of Kinshasa.
- 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: Mount Kamonga Triple: [Haruna Mountains, hasPart, Mount Kamonga]
Generated description
Mount Kamonga is a peak within Japan’s Haruna Mountains, a volcanic range in Gunma Prefecture known for its scenic landscapes and outdoor recreation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mount Kamonga Target entity description: Mount Kamonga is a peak within Japan’s Haruna Mountains, a volcanic range in Gunma Prefecture known for its scenic landscapes and outdoor recreation.
-
A.
Mount Kanobili
Mount Kanobili is a mountain in southern Georgia notable for hosting the Abastumani Astrophysical Observatory.
-
B.
Mount Kaikoma
Mount Kaikoma is a prominent, granite-peaked mountain in Japan’s Southern Alps, known for its rugged terrain and popular hiking and climbing routes.
-
C.
Mount Heha
Mount Heha is the tallest mountain in Burundi, located in the Burundi Highlands near the city of Bujumbura.
-
D.
Mount Noro
Mount Noro is a scenic mountain in Kure, Japan, known for its panoramic views of the Seto Inland Sea and popular hiking and sightseeing opportunities.
-
E.
Mount Amba
Mount Amba is a hill in Kinshasa, Democratic Republic of the Congo, known primarily as the site of the University of Kinshasa.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa9968e88190b14844715ff3aa2a |
completed | April 20, 2026, 10:06 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0733b9f9bc8190a0e3b5b0f1d409d3 |
completed | May 15, 2026, 2:54 p.m. |
| NEDg | Description generation | batch_6a0734809dc48190b4378e168e0e35ec |
completed | May 15, 2026, 2:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07355c10c881909f8a2a3a6b7e7dfe |
completed | May 15, 2026, 3:01 p.m. |
Created at: April 10, 2026, 1:25 p.m.