Triple
T23088604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sangzhi County |
E575681
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object |
Liyuan Town
Liyuan Town is an administrative town that serves as the county seat and political center of Sangzhi County in Hunan Province, China.
|
E1570152
|
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: Liyuan Town | Statement: [Sangzhi County, hasCapital, Liyuan Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Liyuan Town Context triple: [Sangzhi County, hasCapital, Liyuan Town]
-
A.
Luoyuan Town
Luoyuan Town is an urban settlement in Fujian Province, China, serving as the political and economic hub of Luoyuan County.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
Luojing Town
Luojing Town is a suburban township-level division of Shanghai, China, situated within the municipality’s northern Baoshan District.
-
D.
Shangchuan Town
Shangchuan Town is a coastal township-level settlement in Guangdong Province, China, serving as the main administrative and population center for the Shangchuan Island area near Xiachuan Island.
-
E.
Miaohang Town
Miaohang Town is a suburban township-level division in the northern part of Shanghai, China, known for its mix of residential, industrial, and developing urban areas.
- 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: Liyuan Town Triple: [Sangzhi County, hasCapital, Liyuan Town]
Generated description
Liyuan Town is an administrative town that serves as the county seat and political center of Sangzhi County in Hunan Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Liyuan Town Target entity description: Liyuan Town is an administrative town that serves as the county seat and political center of Sangzhi County in Hunan Province, China.
-
A.
Luoyuan Town
Luoyuan Town is an urban settlement in Fujian Province, China, serving as the political and economic hub of Luoyuan County.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
Luojing Town
Luojing Town is a suburban township-level division of Shanghai, China, situated within the municipality’s northern Baoshan District.
-
D.
Shangchuan Town
Shangchuan Town is a coastal township-level settlement in Guangdong Province, China, serving as the main administrative and population center for the Shangchuan Island area near Xiachuan Island.
-
E.
Miaohang Town
Miaohang Town is a suburban township-level division in the northern part of Shanghai, China, known for its mix of residential, industrial, and developing urban areas.
- 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_69e245bf3e3c819086d3448720efc01b |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18da7c54c81908b62d04ab1811b06 |
completed | April 29, 2026, 4:48 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c15b4ddd08190bf04711266d683cf |
completed | May 19, 2026, 7:48 a.m. |
| NEDg | Description generation | batch_6a0c17791f8481909f0f42f9122e0c3a |
completed | May 19, 2026, 7:55 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c1863ea2c81909345cb5701fcd8ed |
completed | May 19, 2026, 7:59 a.m. |
Created at: April 17, 2026, 3:57 p.m.