Triple
T15173453
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Doumen District |
E362546
|
entity |
| Predicate | hasSettlement |
P1068
|
FINISHED |
| Object |
Lianzhou
Lianzhou is a town-level settlement located within Doumen District of Zhuhai in Guangdong Province, China.
|
E1142630
|
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: Lianzhou | Statement: [Doumen District, hasSettlement, Lianzhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lianzhou Context triple: [Doumen District, hasSettlement, Lianzhou]
-
A.
Shaoguan
Shaoguan is a prefecture-level city in northern Guangdong Province, China, known as a regional transport hub and gateway between central and southern China.
-
B.
Haozhou
Haozhou is a historical city in China, known as the birthplace of the Hongwu Emperor, founder of the Ming dynasty.
-
C.
Qingyuan
Qingyuan is a prefecture-level city in northern Guangdong Province, China, known for its karst landscapes, hot springs, and role as a regional transport hub near the Pearl River Delta.
-
D.
Yunfu
Yunfu is a prefecture-level city in western Guangdong Province, China, known for its stone-processing industry and karst landscapes.
-
E.
Heyuan
Heyuan is a prefecture-level city in northeastern Guangdong Province, China, known for its Hakka culture, abundant natural scenery, and large reservoir and river systems.
- 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: Lianzhou Triple: [Doumen District, hasSettlement, Lianzhou]
Generated description
Lianzhou is a town-level settlement located within Doumen District of Zhuhai in Guangdong Province, China.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lianzhou Target entity description: Lianzhou is a town-level settlement located within Doumen District of Zhuhai in Guangdong Province, China.
-
A.
Shaoguan
Shaoguan is a prefecture-level city in northern Guangdong Province, China, known as a regional transport hub and gateway between central and southern China.
-
B.
Haozhou
Haozhou is a historical city in China, known as the birthplace of the Hongwu Emperor, founder of the Ming dynasty.
-
C.
Qingyuan
Qingyuan is a prefecture-level city in northern Guangdong Province, China, known for its karst landscapes, hot springs, and role as a regional transport hub near the Pearl River Delta.
-
D.
Yunfu
Yunfu is a prefecture-level city in western Guangdong Province, China, known for its stone-processing industry and karst landscapes.
-
E.
Heyuan
Heyuan is a prefecture-level city in northeastern Guangdong Province, China, known for its Hakka culture, abundant natural scenery, and large reservoir and river systems.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e006501b488190a2ab09dbf1532571 |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec88c69088190a61f0a5719e99b87 |
completed | May 9, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_69fec93109c08190a3499e4520e31604 |
completed | May 9, 2026, 5:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fecc6fa8f88190aa6956e6e2b1f8ab |
completed | May 9, 2026, 5:55 a.m. |
Created at: April 10, 2026, 3:09 a.m.