Triple
T6511158
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hengshui |
E150136
|
entity |
| Predicate | hasSubdivision |
P747
|
FINISHED |
| Object |
Shenzhou City
Shenzhou City is a county-level city administered by Hengshui in Hebei Province, China, known for its agriculture and developing local industries.
|
E600797
|
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: Shenzhou City | Statement: [Hengshui, hasSubdivision, Shenzhou City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shenzhou City Context triple: [Hengshui, hasSubdivision, Shenzhou City]
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Daiyuan
Daiyuan is a given name most notably associated with Teng Daiyuan, a prominent Chinese Communist revolutionary and political leader.
-
C.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
-
D.
Da Yuan
Da Yuan is the official Chinese name for the Yuan dynasty, the Mongol-ruled imperial dynasty that governed China from the late 13th to the mid-14th century.
-
E.
Zhushikou
Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
- 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: Shenzhou City Triple: [Hengshui, hasSubdivision, Shenzhou City]
Generated description
Shenzhou City is a county-level city administered by Hengshui in Hebei Province, China, known for its agriculture and developing local industries.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Shenzhou City Target entity description: Shenzhou City is a county-level city administered by Hengshui in Hebei Province, China, known for its agriculture and developing local industries.
-
A.
Yuncheng
Yuncheng is a major city in southern Shanxi Province, China, known for its historical sites and role as a regional transportation and economic hub.
-
B.
Daiyuan
Daiyuan is a given name most notably associated with Teng Daiyuan, a prominent Chinese Communist revolutionary and political leader.
-
C.
Licheng
Licheng is the courtesy name of the Daoguang Emperor, a Qing dynasty ruler of China in the early 19th century.
-
D.
Da Yuan
Da Yuan is the official Chinese name for the Yuan dynasty, the Mongol-ruled imperial dynasty that governed China from the late 13th to the mid-14th century.
-
E.
Zhushikou
Zhushikou is a subway station on the Beijing Subway system serving the central area near Beijing’s historic old city.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f3ad7d081909162f1a625fc52b1 |
completed | March 27, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb5dd5b88190b0928b44ebc91609 |
completed | March 27, 2026, 6:24 p.m. |
| NEDg | Description generation | batch_69c6cc96edd08190b0c0f1b49dd64160 |
completed | March 27, 2026, 6:29 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c6cd8d15ec8190be5a8c5e3f201139 |
completed | March 27, 2026, 6:33 p.m. |
Created at: March 27, 2026, 1:43 p.m.