Triple
T2990640
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cagliari |
E80741
|
entity |
| Predicate | twinCity |
P1072
|
FINISHED |
| Object | Nanning |
E185263
|
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: Nanning | Statement: [Cagliari, twinCity, Nanning]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nanning Context triple: [Cagliari, twinCity, Nanning]
-
A.
Nanning
chosen
Nanning is the capital and largest city of China’s Guangxi Zhuang Autonomous Region, known as a key economic hub and “Green City” in the Lingnan cultural area.
-
B.
Anshun
Anshun is a prefecture-level city in southwestern China known for its karst landscapes, including the famous Huangguoshu Waterfall, and its location within Guizhou Province.
-
C.
Guilin
Guilin is a scenic city in southern China’s Guangxi region, famed for its dramatic karst mountains and picturesque Li River landscapes.
-
D.
Guiyang
Guiyang is the capital city of Guizhou Province in southwest China, known for its cool climate, karst landscapes, and role as a regional transportation and industrial hub.
-
E.
Nanping
Nanping is a prefecture-level city in northern Fujian Province, China, known for its mountainous terrain, rich biodiversity, and role as a regional transport and economic hub.
- 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_69ad8b16c3488190b47b6aa7a59a335b |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad99de55208190bc56ecbe08638e5a |
completed | March 8, 2026, 3:46 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b10900bf2481908b7742604c6d75e9 |
completed | March 11, 2026, 6:17 a.m. |
Created at: March 8, 2026, 2:59 p.m.