Triple
T13951238
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Liyang |
E335527
|
entity |
| Predicate | hasNameInChinese |
P4878
|
FINISHED |
| Object |
溧阳
溧阳 is a county-level city in Jiangsu Province, China, known for its scenic Tianmu Lake area and tea production.
|
E1071510
|
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: 溧阳 | Statement: [Liyang, hasNameInChinese, 溧阳]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 溧阳 Context triple: [Liyang, hasNameInChinese, 溧阳]
-
A.
宿迁市
宿迁市 is a prefecture-level city in northern Jiangsu Province, China, known for its historical connection to the Huai River region and rapid modern development.
-
B.
泰州
泰州是中国江苏省中部的一座历史文化名城和重要港口城市，以长江水运、制造业和传统美食而闻名。
-
C.
Jiangning District
Jiangning District is an administrative district in the southern part of Nanjing, China, known for its rapid urban development and role as a key transportation and industrial hub of the city.
-
D.
马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
-
E.
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.
- 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: 溧阳 Triple: [Liyang, hasNameInChinese, 溧阳]
Generated description
溧阳 is a county-level city in Jiangsu Province, China, known for its scenic Tianmu Lake area and tea production.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 溧阳 Target entity description: 溧阳 is a county-level city in Jiangsu Province, China, known for its scenic Tianmu Lake area and tea production.
-
A.
宿迁市
宿迁市 is a prefecture-level city in northern Jiangsu Province, China, known for its historical connection to the Huai River region and rapid modern development.
-
B.
泰州
泰州是中国江苏省中部的一座历史文化名城和重要港口城市，以长江水运、制造业和传统美食而闻名。
-
C.
Jiangning District
Jiangning District is an administrative district in the southern part of Nanjing, China, known for its rapid urban development and role as a key transportation and industrial hub of the city.
-
D.
马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
-
E.
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.
- 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_69d81c6081b88190b53e317c3370c8fe |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e131c608190b4ffdbada24a3208 |
completed | April 14, 2026, 12:07 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fba1cca84881909c7733bbc2609eea |
completed | May 6, 2026, 8:17 p.m. |
| NEDg | Description generation | batch_69fba6af4ed881908cb4b79cfa40977c |
completed | May 6, 2026, 8:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fba71a91fc8190b24185994673b33b |
completed | May 6, 2026, 8:39 p.m. |
Created at: April 9, 2026, 10:17 p.m.