Triple
T23322091
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tokachi region |
E591178
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Makubetsu
Makubetsu is a town in Hokkaido, Japan, known for its agricultural production and scenic rural landscapes in the Tokachi subprefecture.
|
E1580781
|
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: Makubetsu | Statement: [Tokachi region, hasMunicipality, Makubetsu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makubetsu Context triple: [Tokachi region, hasMunicipality, Makubetsu]
-
A.
Makogai
Makogai is a small Fijian island historically known for its former leper colony and rich marine biodiversity.
-
B.
Tsumago
Tsumago is a well-preserved former post town on Japan’s historic Nakasendō route, known for its traditional wooden buildings and Edo-period atmosphere.
-
C.
Kibushi
Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
-
D.
Nakashibetsu
Nakashibetsu is a town in eastern Hokkaido, Japan, known for its dairy farming, expansive rural landscapes, and the easternmost airport in the country.
-
E.
Takabisha
Takabisha is a record-breaking steel roller coaster in Japan renowned for its extremely steep drop and intense thrill elements.
- 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: Makubetsu Triple: [Tokachi region, hasMunicipality, Makubetsu]
Generated description
Makubetsu is a town in Hokkaido, Japan, known for its agricultural production and scenic rural landscapes in the Tokachi subprefecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Makubetsu Target entity description: Makubetsu is a town in Hokkaido, Japan, known for its agricultural production and scenic rural landscapes in the Tokachi subprefecture.
-
A.
Makogai
Makogai is a small Fijian island historically known for its former leper colony and rich marine biodiversity.
-
B.
Tsumago
Tsumago is a well-preserved former post town on Japan’s historic Nakasendō route, known for its traditional wooden buildings and Edo-period atmosphere.
-
C.
Kibushi
Kibushi is a Bantu language spoken primarily in Mayotte, where it serves as one of the island’s main regional languages.
-
D.
Nakashibetsu
Nakashibetsu is a town in eastern Hokkaido, Japan, known for its dairy farming, expansive rural landscapes, and the easternmost airport in the country.
-
E.
Takabisha
Takabisha is a record-breaking steel roller coaster in Japan renowned for its extremely steep drop and intense thrill elements.
- 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_69e25d1effe4819096907f95f610dbff |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1978672148190bb90804361bb896b |
completed | April 29, 2026, 5:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c4ca7d6b48190b3dbdc716333bb8f |
completed | May 19, 2026, 11:42 a.m. |
| NEDg | Description generation | batch_6a0c4fe862988190822c9ed08b8cf3cc |
completed | May 19, 2026, 11:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c50888584819087cce519563da170 |
completed | May 19, 2026, 11:59 a.m. |
Created at: April 17, 2026, 5:07 p.m.