Triple
T2422581
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Shenzhen Airlines |
E53450
|
entity |
| Predicate | focusCity |
P164
|
FINISHED |
| Object | Zhengzhou |
E125528
|
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: Zhengzhou | Statement: [Shenzhen Airlines, focusCity, Zhengzhou]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zhengzhou Context triple: [Shenzhen Airlines, focusCity, Zhengzhou]
-
A.
Zhengzhou
chosen
Zhengzhou is a major city in central China that serves as the capital of Henan Province and an important national transportation and industrial hub.
-
B.
Zhoukou
Zhoukou is a prefecture-level city in eastern Henan Province, China, known as an important agricultural and transportation hub with historical and cultural significance.
-
C.
Xuchang
Xuchang is a historically significant city in central China, known as a former capital during the Three Kingdoms period and now an important industrial and transportation hub.
-
D.
Xinxiang
Xinxiang is a prefecture-level industrial and transportation hub city located in northern Henan Province, China.
-
E.
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.
- 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_69ab495c44d48190b7235b23719bc3f6 |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abc972934481909e05bd6f31162f9d |
completed | March 7, 2026, 6:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aef0a299648190a55e9f2c47bd307f |
completed | March 9, 2026, 4:09 p.m. |
Created at: March 6, 2026, 9:42 p.m.