Triple
T23106021
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yokohama, Kanagawa, Japan |
E576168
|
entity |
| Predicate | famousDistrict |
P15422
|
FINISHED |
| Object |
Kannai
Kannai is a central district of Yokohama known for its historic port-city atmosphere, government and business centers, and mix of classic Western-style buildings and modern urban development.
|
E1572772
|
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: Kannai | Statement: [Yokohama, Kanagawa, Japan, famousDistrict, Kannai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kannai Context triple: [Yokohama, Kanagawa, Japan, famousDistrict, Kannai]
-
A.
Kanai
Kanai was a former municipality in Niigata Prefecture, Japan, that later became part of the city of Sado through a merger.
-
B.
Kan'in
Kan'in is a Japanese princely house that formed one of the collateral branches of the Imperial Family.
-
C.
Kankanay
Kankanay is an Austronesian language spoken by the Kankanaey people of the northern Philippines, particularly in the Cordillera region of Luzon.
-
D.
Kan’onji
Kan’onji is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its historic temples and scenic views of the Seto Inland Sea.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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: Kannai Triple: [Yokohama, Kanagawa, Japan, famousDistrict, Kannai]
Generated description
Kannai is a central district of Yokohama known for its historic port-city atmosphere, government and business centers, and mix of classic Western-style buildings and modern urban development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kannai Target entity description: Kannai is a central district of Yokohama known for its historic port-city atmosphere, government and business centers, and mix of classic Western-style buildings and modern urban development.
-
A.
Kanai
Kanai was a former municipality in Niigata Prefecture, Japan, that later became part of the city of Sado through a merger.
-
B.
Kan'in
Kan'in is a Japanese princely house that formed one of the collateral branches of the Imperial Family.
-
C.
Kankanay
Kankanay is an Austronesian language spoken by the Kankanaey people of the northern Philippines, particularly in the Cordillera region of Luzon.
-
D.
Kan’onji
Kan’onji is a coastal city in western Kagawa Prefecture on Japan’s Shikoku Island, known for its historic temples and scenic views of the Seto Inland Sea.
-
E.
Kōta
Kōta is a town in central Japan known for its manufacturing industries and location within Aichi Prefecture.
- 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_69e245f4af548190898d434a64a1e774 |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e0ad44c81908e1873d4b3860323 |
completed | April 29, 2026, 4:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c307f7e988190b185b11393d9f272 |
completed | May 19, 2026, 9:42 a.m. |
| NEDg | Description generation | batch_6a0c32ab76988190917b76806a6c2c80 |
completed | May 19, 2026, 9:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c3335e83c8190a9bee47e86215c05 |
completed | May 19, 2026, 9:53 a.m. |
Created at: April 17, 2026, 3:58 p.m.