Triple
T8967986
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aleksandr |
E214186
|
entity |
| Predicate | hasShortForm |
P43
|
FINISHED |
| Object | Sanya |
unclear NED1
|
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: Sanya | Statement: [Aleksandr, hasShortForm, Sanya]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sanya Context triple: [Aleksandr, hasShortForm, Sanya]
-
A.
Sanya
Sanya is a major resort city on the southern coast of China’s Hainan Island, known for its tropical climate and popular beach tourism.
-
B.
Haikou
Haikou is the capital and largest city of China’s Hainan Province, known as a key port, commercial hub, and tropical coastal destination.
-
C.
Wanning
Wanning is a county-level coastal city in southeastern Hainan, China, known for its tropical climate, beaches, and surf-friendly bays.
-
D.
Xingsha
Xingsha is a town in Changsha County, Hunan Province, China, known as the modern urban area closest to the famous Mawangdui Han Tombs archaeological site.
-
E.
Beihai
Beihai is a coastal city in China's Guangxi Zhuang Autonomous Region, known for its beaches, maritime trade, and the scenic Silver Beach tourist area.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6764aca48190a5e472d1b6841886 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc95cbc4c8190a3ac582f735eeb35 |
completed | April 3, 2026, 2:06 p.m. |
Created at: March 30, 2026, 7:01 p.m.