Triple
T16404928
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ōmura |
E398401
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
大村市
大村市は、長崎県中部に位置し、長崎空港を擁する交通の要衝として知られる市です。
|
E1211161
|
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: 大村市 | Statement: [Ōmura, hasJapaneseName, 大村市]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 大村市 Context triple: [Ōmura, hasJapaneseName, 大村市]
-
A.
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。
-
B.
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。
-
C.
稲城市
稲城市 is a suburban city in western Tokyo, Japan, known for its residential neighborhoods, greenery, and role as a commuter town for central Tokyo.
-
D.
大東市
大東市は大阪府北河内地域に位置し、住宅地と工業地帯が混在する中核的な都市です。
-
E.
宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
- 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: 大村市 Triple: [Ōmura, hasJapaneseName, 大村市]
Generated description
大村市は、長崎県中部に位置し、長崎空港を擁する交通の要衝として知られる市です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 大村市 Target entity description: 大村市は、長崎県中部に位置し、長崎空港を擁する交通の要衝として知られる市です。
-
A.
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。
-
B.
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。
-
C.
稲城市
稲城市 is a suburban city in western Tokyo, Japan, known for its residential neighborhoods, greenery, and role as a commuter town for central Tokyo.
-
D.
大東市
大東市は大阪府北河内地域に位置し、住宅地と工業地帯が混在する中核的な都市です。
-
E.
宍粟市
宍粟市は、兵庫県西部の中国山地に位置し、豊かな森林資源と自然環境を特徴とする市です。
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d1f16481909adb19dab86dcc72 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003c6094e481909aa7402fd17fedae |
completed | May 10, 2026, 8:05 a.m. |
| NEDg | Description generation | batch_6a003dd7e9d481908822da391112eb39 |
completed | May 10, 2026, 8:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a003eb888e481908eb4ed77f86cf9f3 |
completed | May 10, 2026, 8:15 a.m. |
Created at: April 10, 2026, 5:09 a.m.