Triple
T14124385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lüliang |
E339988
|
entity |
| Predicate | hasAdministrativeDivision |
P747
|
FINISHED |
| Object |
Fenyang
Fenyang is a county-level city in Shanxi Province, China, known for its historical heritage and role in regional commerce and culture.
|
E1086972
|
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: Fenyang | Statement: [Lüliang, hasAdministrativeDivision, Fenyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fenyang Context triple: [Lüliang, hasAdministrativeDivision, Fenyang]
-
A.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
B.
Feicheng
Feicheng is a county-level city in Shandong Province, China, administered by the prefecture-level city of Tai'an.
-
C.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
-
D.
Zhaoyuan
Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
-
E.
Yulin
Yulin is a prefecture-level city in southeastern China known for its role as a regional commercial hub and for its controversial annual dog meat festival.
- 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: Fenyang Triple: [Lüliang, hasAdministrativeDivision, Fenyang]
Generated description
Fenyang is a county-level city in Shanxi Province, China, known for its historical heritage and role in regional commerce and culture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fenyang Target entity description: Fenyang is a county-level city in Shanxi Province, China, known for its historical heritage and role in regional commerce and culture.
-
A.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
B.
Feicheng
Feicheng is a county-level city in Shandong Province, China, administered by the prefecture-level city of Tai'an.
-
C.
Lüliang
Lüliang is a prefecture-level city in western Shanxi Province, China, known for its mountainous terrain and significant coal and energy resources.
-
D.
Zhaoyuan
Zhaoyuan is a county-level city in eastern China's Shandong province, known for its rich gold mining industry and economic development.
-
E.
Yulin
Yulin is a prefecture-level city in southeastern China known for its role as a regional commercial hub and for its controversial annual dog meat festival.
- 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_69d81c6a95b481909e39111e0c1f31ee |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de6096976481909dc79066c5165a50 |
completed | April 14, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd2801198481909f9151ca873bfc56 |
completed | May 8, 2026, 12:02 a.m. |
| NEDg | Description generation | batch_69fd29940d4481908f673ef8eec4302d |
completed | May 8, 2026, 12:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd2a3484888190a314f218cd304c5a |
completed | May 8, 2026, 12:11 a.m. |
Created at: April 9, 2026, 10:22 p.m.