Triple
T17442819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 马鞍山 |
E424699
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object |
花山区
花山区是中国安徽省马鞍山市的主城区之一，以其城市行政、商业和文化功能集中而闻名。
|
E1269102
|
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: 花山区 | Statement: [马鞍山, capital, 花山区]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: 花山区 Context triple: [马鞍山, capital, 花山区]
-
A.
马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
-
B.
铁山区
铁山区 is an industrial and mining-focused urban district under the jurisdiction of Huangshi City in Hubei Province, China.
-
C.
裕安区
裕安区 is an urban district under the administration of the prefecture-level city of Liu'an in Anhui Province, China.
-
D.
叶集区
叶集区是中国安徽省六安市下辖的一个市辖区，以农业和轻工业为主，位于皖西地区。
-
E.
金安区
金安区是安徽省六安市下辖的一个市辖区，位于市区北部，是当地重要的政治、经济和文化中心之一。
- 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: 花山区 Triple: [马鞍山, capital, 花山区]
Generated description
花山区是中国安徽省马鞍山市的主城区之一，以其城市行政、商业和文化功能集中而闻名。
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: 花山区 Target entity description: 花山区是中国安徽省马鞍山市的主城区之一，以其城市行政、商业和文化功能集中而闻名。
-
A.
马鞍山
马鞍山是位于中国安徽省东部、长江沿岸的一座以钢铁工业和山水景观著称的地级市。
-
B.
铁山区
铁山区 is an industrial and mining-focused urban district under the jurisdiction of Huangshi City in Hubei Province, China.
-
C.
裕安区
裕安区 is an urban district under the administration of the prefecture-level city of Liu'an in Anhui Province, China.
-
D.
叶集区
叶集区是中国安徽省六安市下辖的一个市辖区，以农业和轻工业为主，位于皖西地区。
-
E.
金安区
金安区是安徽省六安市下辖的一个市辖区，位于市区北部，是当地重要的政治、经济和文化中心之一。
- 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_69d889db0ba481908402409af3b37917 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e44ff927ec8190995798f569e913ba |
completed | April 19, 2026, 3:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a01aff63d688190b8fa63c8edafc01c |
completed | May 11, 2026, 10:31 a.m. |
| NEDg | Description generation | batch_6a01b0a265348190a884a58f3caf54df |
completed | May 11, 2026, 10:34 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a01b1b0553c8190bb76969fe95154ff |
completed | May 11, 2026, 10:38 a.m. |
Created at: April 10, 2026, 5:47 a.m.