Triple
T5059332
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenyang North Railway Station |
E113983
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Qinhuangdao |
E141777
|
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: Qinhuangdao | Statement: [Shenyang North Railway Station, connectsTo, Qinhuangdao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Qinhuangdao Context triple: [Shenyang North Railway Station, connectsTo, Qinhuangdao]
-
A.
Qinhuangdao
chosen
Qinhuangdao is a coastal port city in northeastern China known for its beaches, seaport, and proximity to the eastern end of the Great Wall.
-
B.
Huludao
Huludao is a coastal city in southwestern Liaoning Province, China, known for its port, shipbuilding industry, and seaside tourism.
-
C.
Lianyungang
Lianyungang is a major coastal city and seaport in eastern China, serving as an important transportation and trade hub on the Yellow Sea.
-
D.
Harbin
Harbin is a major city in northeastern China known for its Russian-influenced architecture and its internationally famous annual ice and snow festival.
-
E.
Dalian
Dalian is a major port city in northeastern China known for its strategic location on the Liaodong Peninsula, maritime trade, and modern urban development.
- 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_69bd443c0c8c81908663b77afb28e165 |
completed | March 20, 2026, 12:57 p.m. |
| NER | Named-entity recognition | batch_69bd7453daac8190b2946702c6c4bd93 |
completed | March 20, 2026, 4:22 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69becfb9bcf48190acac2714c25de725 |
completed | March 21, 2026, 5:04 p.m. |
Created at: March 20, 2026, 1:38 p.m.