Triple
T16061047
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xinyou Coup |
E389611
|
entity |
| Predicate | participant |
P858
|
FINISHED |
| Object |
Sushun
Sushun was a powerful late Qing dynasty statesman and regent known for his conservative policies and his downfall during the Xinyou Coup of 1861.
|
E1193430
|
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: Sushun | Statement: [Xinyou Coup, participant, Sushun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sushun Context triple: [Xinyou Coup, participant, Sushun]
-
A.
Shuda
Shuda is the courtesy name of Zhang Juzheng, the influential Ming dynasty statesman and reformer who served as Grand Secretary under the Wanli Emperor.
-
B.
Subukia
Subukia is a rural town in Kenya known for its agricultural activities and scenic hilly landscape within Nakuru County.
-
C.
Sirsukh
Sirsukh is an ancient walled city near Taxila in present-day Pakistan, built during the Kushan period and known for its distinctive defensive architecture and archaeological remains.
-
D.
Shushary
Shushary is a municipal settlement in the southern part of Saint Petersburg, Russia, known for its residential areas and industrial facilities.
-
E.
Sawa
Sawa is a Japanese surname most prominently associated with Homare Sawa, a legendary Japanese women’s footballer and World Cup winner.
- 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: Sushun Triple: [Xinyou Coup, participant, Sushun]
Generated description
Sushun was a powerful late Qing dynasty statesman and regent known for his conservative policies and his downfall during the Xinyou Coup of 1861.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Sushun Target entity description: Sushun was a powerful late Qing dynasty statesman and regent known for his conservative policies and his downfall during the Xinyou Coup of 1861.
-
A.
Shuda
Shuda is the courtesy name of Zhang Juzheng, the influential Ming dynasty statesman and reformer who served as Grand Secretary under the Wanli Emperor.
-
B.
Subukia
Subukia is a rural town in Kenya known for its agricultural activities and scenic hilly landscape within Nakuru County.
-
C.
Sirsukh
Sirsukh is an ancient walled city near Taxila in present-day Pakistan, built during the Kushan period and known for its distinctive defensive architecture and archaeological remains.
-
D.
Shushary
Shushary is a municipal settlement in the southern part of Saint Petersburg, Russia, known for its residential areas and industrial facilities.
-
E.
Sawa
Sawa is a Japanese surname most prominently associated with Homare Sawa, a legendary Japanese women’s footballer and World Cup winner.
- 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_69d86dae698881908327ef2d67706cb9 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183795100819097be92e6d07dc5b1 |
completed | April 17, 2026, 12:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffe47a92608190993fe7f2c5957019 |
completed | May 10, 2026, 1:50 a.m. |
| NEDg | Description generation | batch_69ffe5ced2dc8190922b910d1a6c08d3 |
completed | May 10, 2026, 1:56 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffe687c204819092a4a8de0b9d624d |
completed | May 10, 2026, 1:59 a.m. |
Created at: April 10, 2026, 4:57 a.m.