Triple
T916628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhang Zhizhong |
E19785
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
|
E116772
|
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: Zhizhong | Statement: [Zhang Zhizhong, givenName, Zhizhong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhizhong Context triple: [Zhang Zhizhong, givenName, Zhizhong]
-
A.
Xinjing
Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
-
B.
Guanggu
Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
-
C.
Shëngjin
Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
-
D.
Kaihui
Kaihui is a Chinese given name most notably borne by Yang Kaihui, the revolutionary and early partner of Mao Zedong.
-
E.
Xiantao
Xiantao is a county-level city in central China’s Hubei province, known for its location on the Jianghan Plain and its role as a regional agricultural and industrial center.
- 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: Zhizhong Triple: [Zhang Zhizhong, givenName, Zhizhong]
Generated description
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Zhizhong Target entity description: Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
-
A.
Xinjing
Xinjing was the capital city of the Japanese puppet state of Manchukuo in northeastern China during the 1930s and early 1940s.
-
B.
Guanggu
Guanggu is a major high-tech development zone in Wuhan, China, known as an innovation hub for the optics and electronics industries.
-
C.
Shëngjin
Shëngjin is a coastal town and port in northwestern Albania on the Adriatic Sea, historically significant for its strategic maritime position.
-
D.
Kaihui
Kaihui is a Chinese given name most notably borne by Yang Kaihui, the revolutionary and early partner of Mao Zedong.
-
E.
Xiantao
Xiantao is a county-level city in central China’s Hubei province, known for its location on the Jianghan Plain and its role as a regional agricultural and industrial center.
- 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_69a4939f91a08190ba68c2c81eab90fe |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b2f8e26c81908b768d3e9e67689d |
completed | March 1, 2026, 9:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac258152cc8190a24456aefd58f19c |
completed | March 7, 2026, 1:17 p.m. |
| NEDg | Description generation | batch_69ac262d13248190a6e9ae3c3c6d2547 |
completed | March 7, 2026, 1:20 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac269165b481909a2fd580c0bf1a70 |
completed | March 7, 2026, 1:22 p.m. |
Created at: March 1, 2026, 7:39 p.m.