Triple
T8950385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kōra, Shiga |
E213329
|
entity |
| Predicate | hasOfficialName |
P66
|
FINISHED |
| Object |
Kōra-chō
Kōra-chō is a small town located in Shiga Prefecture, Japan, known for its rural landscape and traditional local culture.
|
E790595
|
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: Kōra-chō | Statement: [Kōra, Shiga, hasOfficialName, Kōra-chō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kōra-chō Context triple: [Kōra, Shiga, hasOfficialName, Kōra-chō]
-
A.
Kitano-cho
Kitano-cho is a historic district in Kobe, Japan, known for its preserved Western-style residences built by foreign merchants in the late 19th and early 20th centuries.
-
B.
Hamamatsuchō
Hamamatsuchō is a business and transportation district in Tokyo known for its major train and monorail stations, office towers, and proximity to Tokyo Bay.
-
C.
Musashi-Koyama
Musashi-Koyama is a lively neighborhood in Tokyo known for its long covered shopping street, local eateries, and convenient urban living.
-
D.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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: Kōra-chō Triple: [Kōra, Shiga, hasOfficialName, Kōra-chō]
Generated description
Kōra-chō is a small town located in Shiga Prefecture, Japan, known for its rural landscape and traditional local culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kōra-chō Target entity description: Kōra-chō is a small town located in Shiga Prefecture, Japan, known for its rural landscape and traditional local culture.
-
A.
Kitano-cho
Kitano-cho is a historic district in Kobe, Japan, known for its preserved Western-style residences built by foreign merchants in the late 19th and early 20th centuries.
-
B.
Hamamatsuchō
Hamamatsuchō is a business and transportation district in Tokyo known for its major train and monorail stations, office towers, and proximity to Tokyo Bay.
-
C.
Musashi-Koyama
Musashi-Koyama is a lively neighborhood in Tokyo known for its long covered shopping street, local eateries, and convenient urban living.
-
D.
Kanramachi
Kanramachi is a Japanese town known for its cultural and municipal partnership with the Italian town of Certaldo.
-
E.
Akiruno
Akiruno is a city in western Tokyo, Japan, known for its natural scenery, including rivers, forests, and hiking areas.
- 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_69ca839843408190a39069a029a89f15 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc670c7244819084978922a9835bc9 |
completed | April 1, 2026, 12:30 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0b165fb0c81908c79b6ade3cca20e |
completed | April 4, 2026, 6:36 a.m. |
| NEDg | Description generation | batch_69d0b57cf6b88190b5a35b58f88121ae |
completed | April 4, 2026, 6:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d0b5ca593c81908b032fd68ef73897 |
completed | April 4, 2026, 6:55 a.m. |
Created at: March 30, 2026, 6:59 p.m.