Triple
T2490889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xiangtan |
E52036
|
entity |
| Predicate | tourismAttraction |
P530
|
FINISHED |
| Object | Shaoshan |
E42100
|
NE FINISHED |
How this triple was built (2 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: Shaoshan | Statement: [Xiangtan, tourismAttraction, Shaoshan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Shaoshan Context triple: [Xiangtan, tourismAttraction, Shaoshan]
-
A.
Shaoshan
chosen
Shaoshan is a town in Hunan Province, China, best known as the birthplace of Mao Zedong and a significant site of modern Chinese revolutionary history.
-
B.
Ma Sichun
Ma Sichun is a Chinese actress known for her acclaimed film and television roles, including winning the Golden Horse Award for Best Leading Actress.
-
C.
Zhizhong
Zhizhong is a Chinese given name shared by various individuals, including historical and contemporary figures.
-
D.
Xishan
Xishan is the given name of Yan Xishan, a prominent Chinese warlord and political leader active in Shanxi during the early 20th century.
-
E.
Yuelu Mountain
Yuelu Mountain is a scenic and historic mountain area in Changsha, China, known for its ancient academy, temples, and natural landscapes.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd18fe32081909580c6272a6013c5 |
completed | March 7, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af2b8429388190a2d1b1610511ea75 |
completed | March 9, 2026, 8:20 p.m. |
Created at: March 6, 2026, 9:45 p.m.