Triple
T8295642
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kanagawa Prefecture |
E194208
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Ebina
Ebina is a city in central Kanagawa Prefecture, Japan, known as a residential and commercial hub with convenient access to the Tokyo metropolitan area.
|
E812534
|
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: Ebina | Statement: [Kanagawa Prefecture, hasCity, Ebina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ebina Context triple: [Kanagawa Prefecture, hasCity, Ebina]
-
A.
Maishima
Maishima is a man-made island in Osaka, Japan, known for its sports facilities, event venues, and waterfront recreational areas.
-
B.
Ayabe
Ayabe is a small city in the northern part of Japan’s Kyoto Prefecture, known for its rural landscapes, traditional industries, and spiritual retreat centers.
-
C.
Higashikawa
Higashikawa is a town in Hokkaido, Japan, known as a gateway to the Daisetsuzan mountain range and for its scenic natural landscapes.
-
D.
Semboku
Semboku is a city in Akita Prefecture, Japan, known for its historic samurai district in Kakunodate and scenic Lake Tazawa.
-
E.
Koshigaya
Koshigaya is a suburban city in Japan known for its large shopping complexes and residential communities within the Greater Tokyo metropolitan area.
- 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: Ebina Triple: [Kanagawa Prefecture, hasCity, Ebina]
Generated description
Ebina is a city in central Kanagawa Prefecture, Japan, known as a residential and commercial hub with convenient access to the Tokyo metropolitan area.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ebina Target entity description: Ebina is a city in central Kanagawa Prefecture, Japan, known as a residential and commercial hub with convenient access to the Tokyo metropolitan area.
-
A.
Maishima
Maishima is a man-made island in Osaka, Japan, known for its sports facilities, event venues, and waterfront recreational areas.
-
B.
Higashikawa
Higashikawa is a town in Hokkaido, Japan, known as a gateway to the Daisetsuzan mountain range and for its scenic natural landscapes.
-
C.
Ayabe
Ayabe is a small city in the northern part of Japan’s Kyoto Prefecture, known for its rural landscapes, traditional industries, and spiritual retreat centers.
-
D.
Semboku
Semboku is a city in Akita Prefecture, Japan, known for its historic samurai district in Kakunodate and scenic Lake Tazawa.
-
E.
Koshigaya
Koshigaya is a suburban city in Japan known for its large shopping complexes and residential communities within the Greater Tokyo metropolitan area.
- 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_69ca82e50ebc81909aa7b260c76bd757 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7df73d4c81909ad9cf0786eb5a20 |
completed | March 31, 2026, 7:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d181f4123c8190a15ee03b3156c6fd |
completed | April 4, 2026, 9:26 p.m. |
| NEDg | Description generation | batch_69d182ef83e881908a579b6a696ebdc3 |
completed | April 4, 2026, 9:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d1836f1be48190a172834ce9eaafe3 |
completed | April 4, 2026, 9:32 p.m. |
Created at: March 30, 2026, 5:53 p.m.