Triple
T3109979
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shanxi Province |
E64927
|
entity |
| Predicate | hasMajorCity |
P316
|
FINISHED |
| Object |
Linfen
Linfen is a major industrial city in southern Shanxi Province, China, historically known for coal production and severe air pollution.
|
E335875
|
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: Linfen | Statement: [Shanxi Province, hasMajorCity, Linfen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linfen Context triple: [Shanxi Province, hasMajorCity, Linfen]
-
A.
Jincheng
Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
-
B.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
C.
Datong
Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
-
D.
Taiyuan
Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
-
E.
Yulin
Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
- 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: Linfen Triple: [Shanxi Province, hasMajorCity, Linfen]
Generated description
Linfen is a major industrial city in southern Shanxi Province, China, historically known for coal production and severe air pollution.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Linfen Target entity description: Linfen is a major industrial city in southern Shanxi Province, China, historically known for coal production and severe air pollution.
-
A.
Jincheng
Jincheng is a prefecture-level city in southeastern Shanxi Province, China, known for its coal resources and heavy industry.
-
B.
Weinan
Weinan is a prefecture-level city in eastern Shaanxi Province, China, known for its historical sites and location near the Wei River.
-
C.
Datong
Datong is a historic industrial city in northern China known for its coal production and nearby cultural landmarks such as the Yungang Grottoes.
-
D.
Taiyuan
Taiyuan is the capital and largest city of Shanxi Province in northern China, known as an important industrial and transportation hub with a long imperial history.
-
E.
Yulin
Yulin is a prefecture-level city in northern China known for its coal resources and location on the Loess Plateau near the border with Inner Mongolia.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada2a0ab2481908db50738ec3ad0fb |
completed | March 8, 2026, 4:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b24b2a9b648190a74c7691cc7a4959 |
completed | March 12, 2026, 5:12 a.m. |
| NEDg | Description generation | batch_69b24e123ea08190a77a3e7f77107491 |
completed | March 12, 2026, 5:24 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b24e4aff6c81908004ed700a26883c |
completed | March 12, 2026, 5:25 a.m. |
Created at: March 8, 2026, 3:04 p.m.