Triple
T6347412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Similan Islands |
E142778
|
entity |
| Predicate | hasIsland |
P970
|
FINISHED |
| Object |
Ko Huyong
Ko Huyong is one of the Similan Islands in Thailand, known for its protected status and rich marine life.
|
E587843
|
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: Ko Huyong | Statement: [Similan Islands, hasIsland, Ko Huyong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ko Huyong Context triple: [Similan Islands, hasIsland, Ko Huyong]
-
A.
Kang Sheng
Kang Sheng was a prominent Chinese Communist Party leader and chief of Mao Zedong’s secret police, notorious for his role in political purges and the Cultural Revolution.
-
B.
Kong He
Kong He was the father of the ancient Chinese philosopher Confucius and a minor aristocratic military officer in the state of Lu.
-
C.
Yu Minhong
Yu Minhong is a prominent Chinese entrepreneur and educator best known as the founder of New Oriental Education & Technology Group, one of China’s largest private education companies.
-
D.
Zhu Zhanyong
Zhu Zhanyong was a Ming dynasty imperial prince, known primarily as a son of the Hongxi Emperor of China.
-
E.
Hui Xiong
Hui Xiong is a prominent computer scientist and data mining researcher recognized for his influential contributions to knowledge discovery and data analytics.
- 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: Ko Huyong Triple: [Similan Islands, hasIsland, Ko Huyong]
Generated description
Ko Huyong is one of the Similan Islands in Thailand, known for its protected status and rich marine life.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ko Huyong Target entity description: Ko Huyong is one of the Similan Islands in Thailand, known for its protected status and rich marine life.
-
A.
Kang Sheng
Kang Sheng was a prominent Chinese Communist Party leader and chief of Mao Zedong’s secret police, notorious for his role in political purges and the Cultural Revolution.
-
B.
Kong He
Kong He was the father of the ancient Chinese philosopher Confucius and a minor aristocratic military officer in the state of Lu.
-
C.
Yu Minhong
Yu Minhong is a prominent Chinese entrepreneur and educator best known as the founder of New Oriental Education & Technology Group, one of China’s largest private education companies.
-
D.
Zhu Zhanyong
Zhu Zhanyong was a Ming dynasty imperial prince, known primarily as a son of the Hongxi Emperor of China.
-
E.
Hui Xiong
Hui Xiong is a prominent computer scientist and data mining researcher recognized for his influential contributions to knowledge discovery and data analytics.
- 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_69c008d6dcbc8190aa1c2f1fd8916b42 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c067ba2c64819094fa38bb2aeffa6c |
completed | March 22, 2026, 10:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c62d4c78188190a7ceadeedd0e4d15 |
completed | March 27, 2026, 7:10 a.m. |
| NEDg | Description generation | batch_69c62f020d808190a59cbab15a9ca5dc |
completed | March 27, 2026, 7:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c62fbbf58881908e872a6a67676fac |
completed | March 27, 2026, 7:20 a.m. |
Created at: March 22, 2026, 4:31 p.m.