Triple
T14978331
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changde Taohuayuan Airport |
E373510
|
entity |
| Predicate | cityServed |
P82
|
FINISHED |
| Object | Changde |
E76664
|
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: Changde | Statement: [Changde Taohuayuan Airport, cityServed, Changde]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Changde Context triple: [Changde Taohuayuan Airport, cityServed, Changde]
-
A.
Changde
chosen
Changde is a city in northwestern Hunan Province, China, historically significant as a major battleground during the Second Sino-Japanese War.
-
B.
Hengyang
Hengyang is a major industrial and transportation hub city in southern China, located along the Xiang River in the south of Hunan Province.
-
C.
Xiangtan
Xiangtan is a prefecture-level city in central Hunan Province, China, known as an important industrial and commercial hub and for encompassing Shaoshan, the birthplace of Mao Zedong.
-
D.
Huaihua
Huaihua is a prefecture-level city in southwestern Hunan Province, China, known as a regional transportation hub and home to several ethnic minority communities.
-
E.
Zhuzhou
Zhuzhou is a major industrial and transportation hub city in south-central China, known especially for its rail transit and manufacturing industries.
- 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_69d85ccbbcd48190acb56e7cf104d8ad |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6fbd138819092254ea37388026c |
completed | April 15, 2026, 12:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec8741c048190b549782f49969f6a |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 2:51 a.m.