Triple
T19217659
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Guo Xi |
E480527
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object | Bianjing |
—
|
NE NERFINISHED |
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: Bianjing | Statement: [Guo Xi, workLocation, Bianjing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bianjing Context triple: [Guo Xi, workLocation, Bianjing]
-
A.
Kaifeng
chosen
Kaifeng is an ancient city in eastern Henan, China, historically significant as a former capital of several Chinese dynasties and a major cultural and economic center.
-
B.
Zhongdu
Zhongdu was the historical capital city of the Jurchen-led Jin dynasty in northern China, located in what is now part of modern Beijing.
-
C.
Luoyang
Luoyang is one of China’s oldest and most historically significant cities, renowned as an ancient imperial capital and cultural center along the Yellow River.
-
D.
Yuan Dadu
Yuan Dadu was the capital city of the Yuan dynasty in China, located on the site of present-day Beijing and serving as a major political and cultural center of the Mongol-ruled empire.
-
E.
Bianzhou
Bianzhou was an important administrative and strategic city during China’s Later Liang dynasty in the Five Dynasties period.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa3c552c8190b22844e5b7adfc50 |
completed | April 20, 2026, 10:04 a.m. |
Created at: April 10, 2026, 1:23 p.m.