Triple
T10696833
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dalian Airlines |
E252165
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Dalian |
E191200
|
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: Dalian | Statement: [Dalian Airlines, headquartersLocation, Dalian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dalian Context triple: [Dalian Airlines, headquartersLocation, Dalian]
-
A.
Dalian
chosen
Dalian is a major port city in northeastern China known for its strategic location on the Liaodong Peninsula, maritime trade, and modern urban development.
-
B.
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.
-
C.
Shenyang
Shenyang is a major industrial and historical city in northeastern China and the capital of Liaoning Province.
-
D.
Panjin
Panjin is an industrial and oil-producing city in northeastern China, best known for its striking Red Beach wetlands along the Bohai Sea.
-
E.
Huludao
Huludao is a coastal city in southwestern Liaoning Province, China, known for its port, shipbuilding industry, and seaside tourism.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fd89390c8190969ab2b4a79d5818 |
completed | April 9, 2026, 1:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d998e447e0819098e839e9e121a21f |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:12 p.m.