Triple
T12210587
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Longhai Railway |
E290945
|
entity |
| Predicate | terminus |
P388
|
FINISHED |
| Object | Lianyungang |
E152396
|
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: Lianyungang | Statement: [Longhai Railway, terminus, Lianyungang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lianyungang Context triple: [Longhai Railway, terminus, Lianyungang]
-
A.
Lianyungang
chosen
Lianyungang is a major coastal city and seaport in eastern China, serving as an important transportation and trade hub on the Yellow Sea.
-
B.
Laohekou City
Laohekou City is a county-level city in northwestern Hubei Province, China, known as a regional transport and commercial hub under the administration of Xiangyang.
-
C.
Panjin
Panjin is an industrial and oil-producing city in northeastern China, best known for its striking Red Beach wetlands along the Bohai Sea.
-
D.
Fangchenggang
Fangchenggang is a coastal prefecture-level city in southern China known for its port on the Gulf of Tonkin and proximity to the Vietnam border.
-
E.
Yingkou
Yingkou is a coastal port city in northeastern China’s Liaoning Province, known as an important industrial and shipping hub on the Bohai Sea.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c7ed4688190b0546b784e36b0ec |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e5666f48190a28eed761e7b9210 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:51 p.m.