Triple
T2699081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Treaty of Nanking |
E58586
|
entity |
| Predicate | negotiatedBy |
P378
|
FINISHED |
| Object |
Niu Jian
Niu Jian was a Qing dynasty official and diplomat who served as one of the Chinese negotiators of the Treaty of Nanking, which ended the First Opium War.
|
E342072
|
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: Niu Jian | Statement: [Treaty of Nanking, negotiatedBy, Niu Jian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Niu Jian Context triple: [Treaty of Nanking, negotiatedBy, Niu Jian]
-
A.
Yin Jichang
Yin Jichang is a Chinese sculptor best known for designing and creating the iconic Five Rams Statue in Guangzhou.
-
B.
Wang Jian
Wang Jian was a prominent Qin dynasty general whose military campaigns were crucial in the unification of China under Qin rule.
-
C.
Zhang Wenqi
Zhang Wenqi is a Chinese basketball player best known for having played professionally for the Shanghai Sharks in the Chinese Basketball Association.
-
D.
Zhang Jun
Zhang Jun is a Chinese jurist and senior official who serves as the President and Chief Justice of the Supreme People's Court of China.
-
E.
Ma Junren
Ma Junren is a controversial Chinese track coach best known for training world-record-breaking female distance runners in the 1990s amid widespread doping allegations.
- 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: Niu Jian Triple: [Treaty of Nanking, negotiatedBy, Niu Jian]
Generated description
Niu Jian was a Qing dynasty official and diplomat who served as one of the Chinese negotiators of the Treaty of Nanking, which ended the First Opium War.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Niu Jian Target entity description: Niu Jian was a Qing dynasty official and diplomat who served as one of the Chinese negotiators of the Treaty of Nanking, which ended the First Opium War.
-
A.
Yin Jichang
Yin Jichang is a Chinese sculptor best known for designing and creating the iconic Five Rams Statue in Guangzhou.
-
B.
Wang Jian
Wang Jian was a prominent Qin dynasty general whose military campaigns were crucial in the unification of China under Qin rule.
-
C.
Zhang Wenqi
Zhang Wenqi is a Chinese basketball player best known for having played professionally for the Shanghai Sharks in the Chinese Basketball Association.
-
D.
Zhang Jun
Zhang Jun is a Chinese jurist and senior official who serves as the President and Chief Justice of the Supreme People's Court of China.
-
E.
Ma Junren
Ma Junren is a controversial Chinese track coach best known for training world-record-breaking female distance runners in the 1990s amid widespread doping allegations.
- 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_69ab4ac269e481909cb317d79e68b75b |
completed | March 6, 2026, 9:44 p.m. |
| NER | Named-entity recognition | batch_69abda339cf48190b9ae6b99137f005e |
completed | March 7, 2026, 7:56 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28db1ddec8190a6d346afc79cfd8e |
completed | March 12, 2026, 9:56 a.m. |
| NEDg | Description generation | batch_69b28f6d8a948190b9aac1b90de472d6 |
completed | March 12, 2026, 10:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b2c08b196881908e72596d54ab8873 |
completed | March 12, 2026, 1:32 p.m. |
Created at: March 6, 2026, 9:55 p.m.