Triple
T19502698
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yingshang County |
E487942
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Fuyang |
—
|
NE NERFINISHED |
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: Fuyang | Statement: [Yingshang County, locatedIn, Fuyang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fuyang Context triple: [Yingshang County, locatedIn, Fuyang]
-
A.
Fuyang
chosen
Fuyang is a major prefecture-level city in northwestern Anhui Province, China, known as a regional transportation and agricultural hub.
-
B.
Qianjiang
Qianjiang is a city in China known for its regional industry and cultural exchanges, including international town twinning partnerships.
-
C.
Jitao
Jitao is the given name of Dai Jitao, a prominent early 20th-century Chinese politician and close associate of Sun Yat-sen.
-
D.
Huanggang
Huanggang is a significant prefecture-level city in eastern Hubei, China, known for its long history, agricultural production, and proximity to the Yangtze River.
-
E.
Sichun
Sichun is a Chinese given name notably borne by actress Ma Sichun, known for her roles in contemporary Chinese cinema and television.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d9d1c88190b01cd78b8be49384 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6350f8d888190a4809c83522933d4 |
completed | April 20, 2026, 2:15 p.m. |
Created at: April 10, 2026, 1:40 p.m.