Triple
T23454699
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Yanbian |
E567885
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Tumen |
—
|
NE NERFINISHED |
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: Tumen | Statement: [Yanbian, hasCity, Tumen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tumen Context triple: [Yanbian, hasCity, Tumen]
-
A.
Tumen
chosen
Tumen is a border city in northeastern China’s Jilin province, known for its location along the Tumen River facing North Korea.
-
B.
Argun
Argun is a small city in the Chechen Republic of Russia, located just southeast of the regional capital Grozny.
-
C.
Tumen River
The Tumen River is a border river in Northeast Asia that flows between China, North Korea, and Russia before emptying into the Sea of Japan (East Sea).
-
D.
Amursk
Amursk is a small industrial town in Russia’s Far East, situated on the Amur River and known for its timber and pulp-and-paper industries.
-
E.
Il Tumen
Il Tumen is the unicameral regional parliament of the Sakha Republic (Yakutia) in the Russian Federation.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2458b4c888190b1d7998f9862a558 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a695fed08190bfa160e69200546d |
completed | April 29, 2026, 6:35 a.m. |
Created at: April 17, 2026, 5:53 p.m.