Triple
T9919070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tulsa |
E185942
|
entity |
| Predicate | hasSisterCity |
P919
|
FINISHED |
| Object | Beihai |
E56871
|
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: Beihai | Statement: [Tulsa, hasSisterCity, Beihai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beihai Context triple: [Tulsa, hasSisterCity, Beihai]
-
A.
Beihai
chosen
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.
-
B.
Beihai Port
Beihai Port is a major seaport on the southern coast of China that serves as an important maritime gateway for trade in the Beibu Gulf region.
-
C.
Beibu Wan
Beibu Wan is the Chinese name for the Gulf of Tonkin, a large body of water in the South China Sea bordered by China and Vietnam.
-
D.
Wanning
Wanning is a county-level coastal city in southeastern Hainan, China, known for its tropical climate, beaches, and surf-friendly bays.
-
E.
Shekou
Shekou is a coastal district in Shenzhen, China, known as a major transportation and commercial hub with significant port facilities and expatriate communities.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb5685a908190ab3e55b9bf9613f6 |
completed | April 2, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23d2e676c81909e4eed258ecdf053 |
completed | April 5, 2026, 10:45 a.m. |
Created at: March 30, 2026, 8:42 p.m.