Triple
T1625089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State of Chu |
E35122
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Danyang
Danyang was an early capital city of the ancient Chinese State of Chu, significant in the formative period of the Chu kingdom’s political and cultural development.
|
E184511
|
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: Danyang | Statement: [State of Chu, capital, Danyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Danyang Context triple: [State of Chu, capital, Danyang]
-
A.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
B.
Jinling
Jinling is an ancient name for the Chinese city now known as Nanjing, historically renowned as a major political and cultural center.
-
C.
Yangsan
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
-
D.
Zhenjiang
Zhenjiang is a historic port city in eastern China known for its strategic location on the Yangtze River and its rich cultural and culinary heritage.
-
E.
Luyang
Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
- 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: Danyang Triple: [State of Chu, capital, Danyang]
Generated description
Danyang was an early capital city of the ancient Chinese State of Chu, significant in the formative period of the Chu kingdom’s political and cultural development.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Danyang Target entity description: Danyang was an early capital city of the ancient Chinese State of Chu, significant in the formative period of the Chu kingdom’s political and cultural development.
-
A.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
B.
Jinling
Jinling is an ancient name for the Chinese city now known as Nanjing, historically renowned as a major political and cultural center.
-
C.
Yangsan
Yangsan is a city in South Gyeongsang Province, South Korea, known as a growing residential and educational hub near Busan.
-
D.
Zhenjiang
Zhenjiang is a historic port city in eastern China known for its strategic location on the Yangtze River and its rich cultural and culinary heritage.
-
E.
Luyang
Luyang is a historic name associated with the city of Hefei, the capital of Anhui Province in eastern China.
- 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_69a886023194819080a3fccd6e325d0e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a909d19b008190b2224717b2909a78 |
completed | March 5, 2026, 4:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad58d12e808190a1319b1c87bcd22e |
completed | March 8, 2026, 11:09 a.m. |
| NEDg | Description generation | batch_69ad59fede48819084a163ff73fbc1da |
completed | March 8, 2026, 11:14 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad5a85071c8190bab5cefcd918bb8d |
completed | March 8, 2026, 11:16 a.m. |
Created at: March 4, 2026, 7:28 p.m.