Triple
T2808781
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Li Keqiang |
E54117
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Keqiang
Keqiang is the given name of Li Keqiang, who served as the Premier of the People's Republic of China from 2013 to 2023.
|
E302432
|
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: Keqiang | Statement: [Li Keqiang, givenName, Keqiang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keqiang Context triple: [Li Keqiang, givenName, Keqiang]
-
A.
Chongxi
Chongxi is the given name of Bai Chongxi, a prominent Chinese Muslim general and political figure of the Republic of China.
-
B.
Jintao
Jintao is the given name of Hu Jintao, the former President of the People's Republic of China and General Secretary of the Chinese Communist Party.
-
C.
Huang Hai
Huang Hai is the Chinese name for the Yellow Sea, a marginal sea of the western Pacific Ocean located between mainland China and the Korean Peninsula.
-
D.
Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
-
E.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
- 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: Keqiang Triple: [Li Keqiang, givenName, Keqiang]
Generated description
Keqiang is the given name of Li Keqiang, who served as the Premier of the People's Republic of China from 2013 to 2023.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keqiang Target entity description: Keqiang is the given name of Li Keqiang, who served as the Premier of the People's Republic of China from 2013 to 2023.
-
A.
Chongxi
Chongxi is the given name of Bai Chongxi, a prominent Chinese Muslim general and political figure of the Republic of China.
-
B.
Jintao
Jintao is the given name of Hu Jintao, the former President of the People's Republic of China and General Secretary of the Chinese Communist Party.
-
C.
Huang Hai
Huang Hai is the Chinese name for the Yellow Sea, a marginal sea of the western Pacific Ocean located between mainland China and the Korean Peninsula.
-
D.
Xiaochang
Xiaochang is a county in Hubei Province, China, known historically as a rural mission and teaching post where figures like Eric Liddell worked.
-
E.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
- 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_69ab49dcee188190b5c6eca9ae9e3469 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde2fdcf88190a52e515c166ea8f7 |
completed | March 7, 2026, 8:13 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afce97c3008190a966441d719d1d1c |
completed | March 10, 2026, 7:56 a.m. |
| NEDg | Description generation | batch_69afd28600908190ac5defd9f7149e96 |
completed | March 10, 2026, 8:12 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afe2ad33308190bcef8a3188601347 |
completed | March 10, 2026, 9:21 a.m. |
Created at: March 6, 2026, 9:59 p.m.