Triple
T4038672
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niseko |
E83888
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Hanazono
Hanazono is a popular ski and outdoor recreation area within the Niseko resort region of Hokkaido, Japan, known for its powder snow and winter sports facilities.
|
E498959
|
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: Hanazono | Statement: [Niseko, hasPart, Hanazono]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hanazono Context triple: [Niseko, hasPart, Hanazono]
-
A.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
B.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
C.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
D.
Kiyokawa
Kiyokawa is a small rural village in Kanagawa Prefecture, Japan, known for its mountainous scenery and outdoor recreation.
-
E.
Hatagaya
Hatagaya is a residential neighborhood in Tokyo known for its convenient access to central Shibuya and its mix of quiet local streets and urban amenities.
- 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: Hanazono Triple: [Niseko, hasPart, Hanazono]
Generated description
Hanazono is a popular ski and outdoor recreation area within the Niseko resort region of Hokkaido, Japan, known for its powder snow and winter sports facilities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Hanazono Target entity description: Hanazono is a popular ski and outdoor recreation area within the Niseko resort region of Hokkaido, Japan, known for its powder snow and winter sports facilities.
-
A.
Takayoshi
Takayoshi is a Japanese given name notably borne by Kido Takayoshi, a key samurai and statesman of the Meiji Restoration.
-
B.
Takaishi
Takaishi is a city in Osaka Prefecture, Japan, known as a small industrial and residential hub within the Osaka metropolitan area.
-
C.
Takanami
Takanami was a Japanese destroyer of the Imperial Japanese Navy during World War II, notable for being sunk in the Battle of Tassafaronga in 1942.
-
D.
Kiyokawa
Kiyokawa is a small rural village in Kanagawa Prefecture, Japan, known for its mountainous scenery and outdoor recreation.
-
E.
Hatagaya
Hatagaya is a residential neighborhood in Tokyo known for its convenient access to central Shibuya and its mix of quiet local streets and urban amenities.
- 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_69aed92f7cf0819098e0539bdcc3767f |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefb37e24c81908d6357ab8ba5388d |
completed | March 9, 2026, 4:54 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bed8f8a8a08190b403b8c3caf20009 |
completed | March 21, 2026, 5:44 p.m. |
| NEDg | Description generation | batch_69bed9c4bd98819089c9d656379a959d |
completed | March 21, 2026, 5:47 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69beda1519348190a09e01ce4464bccc |
completed | March 21, 2026, 5:49 p.m. |
Created at: March 9, 2026, 3:37 p.m.