Triple
T22563098
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pingtan County |
E557868
|
entity |
| Predicate | hasCapital |
P204
|
FINISHED |
| Object |
Tancheng Town
Tancheng Town is the administrative center and main urban hub of Pingtan County in Fujian Province, China.
|
E1543504
|
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: Tancheng Town | Statement: [Pingtan County, hasCapital, Tancheng Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tancheng Town Context triple: [Pingtan County, hasCapital, Tancheng Town]
-
A.
Chengguan town
Chengguan town is the main urban hub and political, economic, and cultural center of Yuzhong County in Gansu Province, China.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
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.
-
D.
Gaotangling town
Gaotangling town is an urban township that serves as the main commercial and administrative center of Wangcheng County in Hunan Province, China.
-
E.
Gaojing Town
Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, 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: Tancheng Town Triple: [Pingtan County, hasCapital, Tancheng Town]
Generated description
Tancheng Town is the administrative center and main urban hub of Pingtan County in Fujian Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tancheng Town Target entity description: Tancheng Town is the administrative center and main urban hub of Pingtan County in Fujian Province, China.
-
A.
Chengguan town
Chengguan town is the main urban hub and political, economic, and cultural center of Yuzhong County in Gansu Province, China.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
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.
-
D.
Gaotangling town
Gaotangling town is an urban township that serves as the main commercial and administrative center of Wangcheng County in Hunan Province, China.
-
E.
Gaojing Town
Gaojing Town is an administrative town located within Baoshan District in the northern part of Shanghai, 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_69e11e5ae4ac8190b1f503457603d969 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15fa7a828819096804ac928e2aaf9 |
completed | April 29, 2026, 1:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b2d70f01c81909e861a91dbc74ecc |
completed | May 18, 2026, 3:17 p.m. |
| NEDg | Description generation | batch_6a0b364801cc81908204a937c1099728 |
completed | May 18, 2026, 3:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b37a1ecc08190ac89e862582833a0 |
completed | May 18, 2026, 4 p.m. |
Created at: April 16, 2026, 8:52 p.m.