Triple
T1654797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jiangxi Province |
E35773
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Nanchang |
E66806
|
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: Nanchang | Statement: [Jiangxi Province, hasCity, Nanchang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanchang Context triple: [Jiangxi Province, hasCity, Nanchang]
-
A.
Nanchang
chosen
Nanchang is the capital and largest city of Jiangxi Province in southeastern China, known as an important regional industrial and transportation hub.
-
B.
Huangshi
Huangshi is an industrial city in eastern Hubei Province, China, known for its steel production and location along the Yangtze River.
-
C.
Anqing
Anqing is a prefecture-level city in southwestern Anhui Province, China, known historically as a regional political and military center along the Yangtze River.
-
D.
Yichun
Yichun is a prefecture-level city in western Jiangxi Province, China, known for its natural scenery, hot springs, and cultural heritage.
-
E.
Hefei
Hefei is the capital and largest city of Anhui Province in eastern China, known as a major industrial, scientific, and educational center.
- 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_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a8b597c81908a62b41718d85df6 |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3a390bc8190891ebd5d8a48d818 |
completed | March 9, 2026, 11:48 a.m. |
Created at: March 4, 2026, 7:29 p.m.