Triple
T4628389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sui dynasty |
E101154
|
entity |
| Predicate | capital |
P234
|
FINISHED |
| Object | Daxing |
E89779
|
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: Daxing | Statement: [Sui dynasty, capital, Daxing]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daxing Context triple: [Sui dynasty, capital, Daxing]
-
A.
Yizhuang
Yizhuang is a rapidly developing suburban area in southeastern Beijing known for its economic and technological development zone and growing residential communities.
-
B.
Daxing District
chosen
Daxing District is a rapidly developing suburban district in southern Beijing, China, known for hosting the major Beijing Daxing International Airport and large-scale urban expansion.
-
C.
Chaoyang
Chaoyang is a prefecture-level city in western Liaoning Province, China, known for its historical sites and role as a regional transportation and agricultural center.
-
D.
Changping District
Changping District is a suburban district in the northern part of Beijing, China, known for its historical sites and scenic mountainous landscapes.
-
E.
Lingang
Lingang is a rapidly developing industrial and high-tech district in Shanghai, China, known for hosting major manufacturing facilities such as Tesla’s Gigafactory Shanghai.
- 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_69bd43d0497c8190ac23c65c5804846a |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd5a300e6081909fa9f504aada33ea |
completed | March 20, 2026, 2:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bf28ea49588190bf1bc4cb3ca4dcee |
completed | March 21, 2026, 11:25 p.m. |
Created at: March 20, 2026, 1:13 p.m.