Triple
T22284251
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luoyuan County |
E550816
|
entity |
| Predicate | administrativeCenter |
P1474
|
FINISHED |
| Object |
Luoyuan Town
Luoyuan Town is an urban settlement in Fujian Province, China, serving as the political and economic hub of Luoyuan County.
|
E1528747
|
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: Luoyuan Town | Statement: [Luoyuan County, administrativeCenter, Luoyuan Town]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luoyuan Town Context triple: [Luoyuan County, administrativeCenter, Luoyuan Town]
-
A.
Luojing Town
Luojing Town is a suburban township-level division of Shanghai, China, situated within the municipality’s northern Baoshan District.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
Luodian Town
Luodian Town is a suburban town in Shanghai, China, known for its residential communities and local commerce within Baoshan District.
-
D.
Jishi Town
Jishi Town is the administrative and economic center of Xunhua Salar Autonomous County in Qinghai Province, China.
-
E.
Zhushan Town
Zhushan Town is the main urban and political hub of Zhushan County in Hubei Province, China, serving as its central seat of local government and administration.
- 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: Luoyuan Town Triple: [Luoyuan County, administrativeCenter, Luoyuan Town]
Generated description
Luoyuan Town is an urban settlement in Fujian Province, China, serving as the political and economic hub of Luoyuan County.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Luoyuan Town Target entity description: Luoyuan Town is an urban settlement in Fujian Province, China, serving as the political and economic hub of Luoyuan County.
-
A.
Luojing Town
Luojing Town is a suburban township-level division of Shanghai, China, situated within the municipality’s northern Baoshan District.
-
B.
Yingshang Town
Yingshang Town is the main urban and political hub of Yingshang County in Anhui Province, China.
-
C.
Luodian Town
Luodian Town is a suburban town in Shanghai, China, known for its residential communities and local commerce within Baoshan District.
-
D.
Jishi Town
Jishi Town is the administrative and economic center of Xunhua Salar Autonomous County in Qinghai Province, China.
-
E.
Zhushan Town
Zhushan Town is the main urban and political hub of Zhushan County in Hubei Province, China, serving as its central seat of local government and administration.
- 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_69e11e44d538819097c6b8f333af3352 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15605a8448190906a0ab9ffa4260b |
completed | April 29, 2026, 12:51 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0abcac163c8190b0bb8673ed46c046 |
completed | May 18, 2026, 7:15 a.m. |
| NEDg | Description generation | batch_6a0abe56d038819083b2ce2b87e067a2 |
completed | May 18, 2026, 7:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0abf7e6d208190807d4c1316d5bd0d |
completed | May 18, 2026, 7:27 a.m. |
Created at: April 16, 2026, 8:40 p.m.