Triple
T11831752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorgon |
E281407
|
entity |
| Predicate | roleInCaptureOfBeijing |
P101724
|
FINISHED |
| Object | commander of Qing forces entering Beijing |
—
|
LITERAL FINISHED |
How this triple was built (2 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: commander of Qing forces entering Beijing | Statement: [Dorgon, roleInCaptureOfBeijing, commander of Qing forces entering Beijing]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInCaptureOfBeijing Context triple: [Dorgon, roleInCaptureOfBeijing, commander of Qing forces entering Beijing]
-
A.
roleInManchuria
Indicates that an entity holds or held a specific role, position, or function in the context of Manchuria.
-
B.
roleDuringSecondSinoJapaneseWar
Indicates the specific role, position, or function an entity held or performed during the Second Sino-Japanese War.
-
C.
ChineseLeader
Indicates that the subject is a person who holds or has held a top political leadership position in China.
-
D.
formerUNSeatHolderForChina
Indicates that the subject previously held China’s seat in the United Nations before the current recognized representative.
-
E.
roleInKoreanWar
Indicates the specific function, position, or involvement an entity had during the Korean War.
- F. None of above. chosen
Provenance (4 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_69d6ab276f8c8190b1966a0ef11349ac |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a62c95988190a45dbaa7001c8846 |
completed | April 10, 2026, 7:26 a.m. |
| PD | Predicate disambiguation | batch_69d8a251fc08819095933f1d13c3b742 |
completed | April 10, 2026, 7:10 a.m. |
| PDg | Predicate description generation | batch_69d8a43cc0c881909fed7cd759fe90b1 |
completed | April 10, 2026, 7:18 a.m. |
Created at: April 8, 2026, 9:43 p.m.