Triple
T7439465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weifang |
E171707
|
entity |
| Predicate | hasDistrict |
P459
|
FINISHED |
| Object |
Fangzi District
Fangzi District is an urban administrative district within the prefecture-level city of Weifang in Shandong Province, eastern China.
|
E691030
|
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: Fangzi District | Statement: [Weifang, hasDistrict, Fangzi District]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fangzi District Context triple: [Weifang, hasDistrict, Fangzi District]
-
A.
Zhifu District
Zhifu District is the central urban district and administrative, commercial, and cultural core of Yantai in Shandong Province, China.
-
B.
Shuangxi District
Shuangxi District is a rural, mountainous district in eastern New Taipei City, Taiwan, known for its rivers, old streets, and natural scenery.
-
C.
Xiangfang District
Xiangfang District is an urban district of Harbin in Heilongjiang Province, China, known for its industrial base and role in the city's economic development.
-
D.
Jinyuan District
Jinyuan District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
-
E.
Yanfeng District
Yanfeng District is an urban administrative district of the prefecture-level city of Hengyang in Hunan Province, 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: Fangzi District Triple: [Weifang, hasDistrict, Fangzi District]
Generated description
Fangzi District is an urban administrative district within the prefecture-level city of Weifang in Shandong Province, eastern China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fangzi District Target entity description: Fangzi District is an urban administrative district within the prefecture-level city of Weifang in Shandong Province, eastern China.
-
A.
Zhifu District
Zhifu District is the central urban district and administrative, commercial, and cultural core of Yantai in Shandong Province, China.
-
B.
Shuangxi District
Shuangxi District is a rural, mountainous district in eastern New Taipei City, Taiwan, known for its rivers, old streets, and natural scenery.
-
C.
Xiangfang District
Xiangfang District is an urban district of Harbin in Heilongjiang Province, China, known for its industrial base and role in the city's economic development.
-
D.
Jinyuan District
Jinyuan District is an urban administrative district of Taiyuan, the capital city of Shanxi Province in northern China.
-
E.
Yanfeng District
Yanfeng District is an urban administrative district of the prefecture-level city of Hengyang in Hunan Province, 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_69c68a64228c8190affaec2a8127ce7b |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f34c28648190a426b5d7623b41e8 |
completed | March 27, 2026, 9:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c966100ffc8190873c6246759b0aa0 |
completed | March 29, 2026, 5:49 p.m. |
| NEDg | Description generation | batch_69c966b7bc3c8190a2839a0367e939a9 |
completed | March 29, 2026, 5:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c96727575c81909eb3877cd5f3679d |
completed | March 29, 2026, 5:53 p.m. |
Created at: March 27, 2026, 3:13 p.m.