Triple
T10684099
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kujūkuri Beach |
E251829
|
entity |
| Predicate | hasJapaneseName |
P9882
|
FINISHED |
| Object |
九十九里浜
九十九里浜は、千葉県東部の太平洋沿岸に約60キロメートルにわたって続く日本有数の長大な砂浜海岸です。
|
E878965
|
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: 九十九里浜 | Statement: [Kujūkuri Beach, hasJapaneseName, 九十九里浜]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 九十九里浜 Context triple: [Kujūkuri Beach, hasJapaneseName, 九十九里浜]
-
A.
溪口
溪口是位于中国浙江省奉化区的一个历史悠久的小镇，以自然风光和民俗文化闻名。
-
B.
慈湖
慈湖 is a scenic lake in Taiwan known for its tranquil natural surroundings and historical significance as the site of the Cihu Mausoleum of former President Chiang Kai-shek.
-
C.
西堤
西堤是位于北京颐和园昆明湖西侧的一条仿杭州苏堤而建的长堤景观，以其连桥叠景和湖山胜色著称。
-
D.
富春江
富春江是中国浙江省境内一条以秀丽山水风光和人文景观著称的重要河流与旅游胜地。
-
E.
黄浦江
黄浦江是流经中国上海市中心、将浦西与浦东分隔开并最终汇入长江的重要城市河流。
- 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: 九十九里浜 Triple: [Kujūkuri Beach, hasJapaneseName, 九十九里浜]
Generated description
九十九里浜は、千葉県東部の太平洋沿岸に約60キロメートルにわたって続く日本有数の長大な砂浜海岸です。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 九十九里浜 Target entity description: 九十九里浜は、千葉県東部の太平洋沿岸に約60キロメートルにわたって続く日本有数の長大な砂浜海岸です。
-
A.
溪口
溪口是位于中国浙江省奉化区的一个历史悠久的小镇，以自然风光和民俗文化闻名。
-
B.
慈湖
慈湖 is a scenic lake in Taiwan known for its tranquil natural surroundings and historical significance as the site of the Cihu Mausoleum of former President Chiang Kai-shek.
-
C.
西堤
西堤是位于北京颐和园昆明湖西侧的一条仿杭州苏堤而建的长堤景观，以其连桥叠景和湖山胜色著称。
-
D.
富春江
富春江是中国浙江省境内一条以秀丽山水风光和人文景观著称的重要河流与旅游胜地。
-
E.
黄浦江
黄浦江是流经中国上海市中心、将浦西与浦东分隔开并最终汇入长江的重要城市河流。
- 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_69d6aa5bd7c08190a816e733b4045c23 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fcc5134c8190bcb1d96a32634c17 |
completed | April 9, 2026, 1:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9888cf7b481909de6a4fecb48cf4b |
completed | April 10, 2026, 11:32 p.m. |
| NEDg | Description generation | batch_69d98aeb82988190a17b009c74279423 |
completed | April 10, 2026, 11:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d98c2aae048190b348e5614ff23f03 |
completed | April 10, 2026, 11:47 p.m. |
Created at: April 8, 2026, 9:10 p.m.