Triple
T10002075
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luoyang |
E197350
|
entity |
| Predicate | pinyinName |
P9333
|
FINISHED |
| Object |
Luòyáng
Luòyáng is an ancient Chinese city in Henan Province that served as the capital for multiple dynasties and is renowned as one of the cradles of Chinese civilization.
|
E842791
|
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: Luòyáng | Statement: [Luoyang, pinyinName, Luòyáng]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luòyáng Context triple: [Luoyang, pinyinName, Luòyáng]
-
A.
Liúyáng
Liúyáng is the Hanyu Pinyin romanization of the Chinese city name Liuyang, located in Hunan Province, China.
-
B.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
-
C.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
-
D.
Jianye
Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
-
E.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
- 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: Luòyáng Triple: [Luoyang, pinyinName, Luòyáng]
Generated description
Luòyáng is an ancient Chinese city in Henan Province that served as the capital for multiple dynasties and is renowned as one of the cradles of Chinese civilization.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Luòyáng Target entity description: Luòyáng is an ancient Chinese city in Henan Province that served as the capital for multiple dynasties and is renowned as one of the cradles of Chinese civilization.
-
A.
Liúyáng
Liúyáng is the Hanyu Pinyin romanization of the Chinese city name Liuyang, located in Hunan Province, China.
-
B.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
-
C.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
-
D.
Jianye
Jianye is an ancient name for the city now known as Nanjing, a historically significant capital in several Chinese dynasties.
-
E.
Luzhi
Luzhi is an ancient canal town near Suzhou in China, renowned for its well-preserved waterways, stone bridges, and traditional Jiangnan architecture.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc9078788190a4e75dd7ff830c63 |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2cb8b92b0819081e5ac52e2f4f27e |
completed | April 5, 2026, 8:52 p.m. |
| NEDg | Description generation | batch_69d2cd242ed8819097895cb15cbb5d47 |
completed | April 5, 2026, 8:59 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d2d1204f008190a9349c071c1e8d19 |
completed | April 5, 2026, 9:16 p.m. |
Created at: March 30, 2026, 8:51 p.m.