Triple
T8950384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kōra, Shiga |
E213329
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
甲良町
甲良町は、滋賀県犬上郡に位置する、歴史的な寺院や田園風景が広がる小規模な町です。
|
E768609
|
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: 甲良町 | Statement: [Kōra, Shiga, hasJapaneseName, 甲良町]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 甲良町 Context triple: [Kōra, Shiga, hasJapaneseName, 甲良町]
-
A.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
B.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
C.
木津川市
木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
-
D.
豊郷町
豊郷町は、滋賀県犬上郡に位置し、アニメ『けいおん！』の舞台モデルとして知られる小さな町です。
-
E.
Misato Town
Misato Town is a rural municipality in northeastern Japan known for its agricultural landscape and location within Miyagi 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: 甲良町 Triple: [Kōra, Shiga, hasJapaneseName, 甲良町]
Generated description
甲良町は、滋賀県犬上郡に位置する、歴史的な寺院や田園風景が広がる小規模な町です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 甲良町 Target entity description: 甲良町は、滋賀県犬上郡に位置する、歴史的な寺院や田園風景が広がる小規模な町です。
-
A.
瑞穂町
瑞穂町は、日本の東京都西多摩郡に位置する住宅地と自然が混在する町です。
-
B.
高千穂町
高千穂町は、宮崎県北西部に位置し、神話の里として知られる峡谷や高千穂神社などの観光名所で有名な町です。
-
C.
木津川市
木津川市は、京都府南部に位置し、奈良県に隣接する住宅都市・歴史観光地として発展している市です。
-
D.
豊郷町
豊郷町は、滋賀県犬上郡に位置し、アニメ『けいおん！』の舞台モデルとして知られる小さな町です。
-
E.
Misato Town
Misato Town is a rural municipality in northeastern Japan known for its agricultural landscape and location within Miyagi 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_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_69cfc20a5ab481909e10f3abf679ec4c |
completed | April 3, 2026, 1:35 p.m. |
| NEDg | Description generation | batch_69cfc2d295c48190952486e6f44cd74f |
completed | April 3, 2026, 1:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfc722921881908978147e4cc6875c |
completed | April 3, 2026, 1:56 p.m. |
Created at: March 30, 2026, 6:59 p.m.