Triple
T8874573
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onekotan |
E211240
|
entity |
| Predicate | separatedFrom |
P243
|
FINISHED |
| Object | Makanrushi |
E739061
|
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: Makanrushi | Statement: [Onekotan, separatedFrom, Makanrushi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Makanrushi Context triple: [Onekotan, separatedFrom, Makanrushi]
-
A.
Makanrushi
chosen
Makanrushi is an uninhabited volcanic island in the Kuril Islands chain of the northwest Pacific Ocean, belonging to Russia.
-
B.
Ma Kai
Ma Kai is a Chinese politician who served as a Vice Premier of the State Council and played a key role in the country’s economic and financial policymaking.
-
C.
Wazwan
Wazwan is a lavish multi-course feast from Kashmir, renowned for its rich meat-based dishes and central role in weddings and special celebrations.
-
D.
Dashi
Dashi is a metro station on Guangzhou's Line 7 serving the Panyu District area of Guangzhou, China.
-
E.
Bhakna
Bhakna is an Indian surname notably associated with Sohan Singh Bhakna, a prominent early leader of the Ghadar Party in the Indian independence movement.
- 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_69ca838e78748190934d82db3104f855 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc614451d081908804430a72d00edf |
completed | April 1, 2026, 12:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfa0f9714c8190909587eecbb10e1a |
completed | April 3, 2026, 11:14 a.m. |
Created at: March 30, 2026, 6:52 p.m.