Triple
T6026585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cangzhou |
E134196
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
Yunhe District
Yunhe District is an urban administrative district that serves as the central area and governmental seat of Cangzhou in Hebei Province, China.
|
E626132
|
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: Yunhe District | Statement: [Cangzhou, capital, Yunhe District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yunhe District Context triple: [Cangzhou, capital, Yunhe District]
-
A.
Neihu District
Neihu District is a suburban and technology-focused district in northeastern Taipei, Taiwan, known for its science parks, residential communities, and natural scenery.
-
B.
Yingdong District
Yingdong District is an urban administrative district of Fuyang City in Anhui Province, China.
-
C.
Xialu District
Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
-
D.
Fengrun District
Fengrun District is an administrative district under the jurisdiction of the prefecture-level city of Tangshan in Hebei Province, China.
-
E.
Dianjun District
Dianjun District is an urban district of Yichang City in Hubei Province, central China, situated along the Yangtze River and known for its role in the region’s transportation and industry.
- 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: Yunhe District Triple: [Cangzhou, capital, Yunhe District]
Generated description
Yunhe District is an urban administrative district that serves as the central area and governmental seat of Cangzhou in Hebei Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Yunhe District Target entity description: Yunhe District is an urban administrative district that serves as the central area and governmental seat of Cangzhou in Hebei Province, China.
-
A.
Neihu District
Neihu District is a suburban and technology-focused district in northeastern Taipei, Taiwan, known for its science parks, residential communities, and natural scenery.
-
B.
Yingdong District
Yingdong District is an urban administrative district of Fuyang City in Anhui Province, China.
-
C.
Xialu District
Xialu District is an urban administrative district of the prefecture-level city of Huangshi in Hubei Province, China.
-
D.
Fengrun District
Fengrun District is an administrative district under the jurisdiction of the prefecture-level city of Tangshan in Hebei Province, China.
-
E.
Dianjun District
Dianjun District is an urban district of Yichang City in Hubei Province, central China, situated along the Yangtze River and known for its role in the region’s transportation and industry.
- 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_69c0087515148190a97475d412563865 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0560cdc308190b25ca8ecb42c4e4f |
completed | March 22, 2026, 8:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7421871888190aab99c6c5f6c147d |
completed | March 28, 2026, 2:51 a.m. |
| NEDg | Description generation | batch_69c742fdbd888190bc5b5ec5e2cbcd6a |
completed | March 28, 2026, 2:54 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c7436b4aa08190aa5bb222c3c45fa9 |
completed | March 28, 2026, 2:56 a.m. |
Created at: March 22, 2026, 4:07 p.m.