Triple
T19529995
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heaven Lake |
E488630
|
entity |
| Predicate | hasNearbyCity |
P350
|
FINISHED |
| Object | Samjiyon |
—
|
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: Samjiyon | Statement: [Heaven Lake, hasNearbyCity, Samjiyon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samjiyon Context triple: [Heaven Lake, hasNearbyCity, Samjiyon]
-
A.
Samjiyon
chosen
Samjiyon is a North Korean city in Ryanggang Province known for its proximity to Mount Paektu and its development as a showcase model city.
-
B.
Sijjin
Sijjin is an Islamic term referring to a record or register in which the deeds of the wicked are inscribed and a place associated with severe punishment in the Hereafter.
-
C.
Sinwonsa
Sinwonsa is a historic Buddhist temple in South Korea, known as one of the principal temples on Mount Gyeryong and noted for its traditional architecture and serene natural setting.
-
D.
Yeji
Yeji is a town in central Ghana situated on the shores of Lake Volta, known as a local fishing and trading hub.
-
E.
Samiyam
Samiyam is an American hip-hop producer and beatmaker known for his off-kilter, synth-heavy instrumentals and association with the Los Angeles beat scene.
- 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_69d8e8da8bec819081f400199491ccc3 |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e6363dfd6c8190aaa0b374184965bb |
completed | April 20, 2026, 2:20 p.m. |
Created at: April 10, 2026, 1:41 p.m.