Triple
T8257152
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mizuho |
E193098
|
entity |
| Predicate | neighboringArea |
P33892
|
FINISHED |
| Object |
Iruma
Iruma is a city in Saitama Prefecture, Japan, known for its residential suburbs, Sayama Hills greenery, and tea cultivation.
|
E727343
|
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: Iruma | Statement: [Mizuho, neighboringArea, Iruma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iruma Context triple: [Mizuho, neighboringArea, Iruma]
-
A.
Aoyama
Aoyama is an upscale district in Tokyo known for its high-end fashion boutiques, modern architecture, and trendy cafes and galleries.
-
B.
Hidaka
Hidaka was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
-
C.
Gushikawa
Gushikawa was a former city in Okinawa Prefecture, Japan, that later became part of the modern city of Uruma.
-
D.
Tateyama
Tateyama is a coastal city in southern Chiba Prefecture, Japan, known for its mild climate, beaches, and views of Mount Fuji across Tokyo Bay.
-
E.
Yamakita
Yamakita is a rural town in Kanagawa Prefecture, Japan, known for its mountainous terrain, hot springs, and access to outdoor activities such as hiking and river sports.
- 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: Iruma Triple: [Mizuho, neighboringArea, Iruma]
Generated description
Iruma is a city in Saitama Prefecture, Japan, known for its residential suburbs, Sayama Hills greenery, and tea cultivation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Iruma Target entity description: Iruma is a city in Saitama Prefecture, Japan, known for its residential suburbs, Sayama Hills greenery, and tea cultivation.
-
A.
Aoyama
Aoyama is an upscale district in Tokyo known for its high-end fashion boutiques, modern architecture, and trendy cafes and galleries.
-
B.
Hidaka
Hidaka was a former town in Hyōgo Prefecture, Japan, that later became part of the expanded city of Toyooka through municipal merger.
-
C.
Gushikawa
Gushikawa was a former city in Okinawa Prefecture, Japan, that later became part of the modern city of Uruma.
-
D.
Tateyama
Tateyama is a coastal city in southern Chiba Prefecture, Japan, known for its mild climate, beaches, and views of Mount Fuji across Tokyo Bay.
-
E.
Yamakita
Yamakita is a rural town in Kanagawa Prefecture, Japan, known for its mountainous terrain, hot springs, and access to outdoor activities such as hiking and river sports.
- 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_69ca82dfad9c8190b8cd18fb89f50f40 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb78fb91d08190904c59ccc0cd444a |
completed | March 31, 2026, 7:34 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cdc6c1451881909cfed1e27b57847c |
completed | April 2, 2026, 1:30 a.m. |
| NEDg | Description generation | batch_69cdcb8cbd3c8190b467ecbcf55231e9 |
completed | April 2, 2026, 1:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cdccff097c819099a33612504468e1 |
completed | April 2, 2026, 1:57 a.m. |
Created at: March 30, 2026, 5:49 p.m.