Triple
T20748145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinju-si |
E510642
|
entity |
| Predicate | nearCity |
P350
|
FINISHED |
| Object | Haman-gun |
—
|
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: Haman-gun | Statement: [Jinju-si, nearCity, Haman-gun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haman-gun Context triple: [Jinju-si, nearCity, Haman-gun]
-
A.
Haman-gun
chosen
Haman-gun is a rural county in South Gyeongsang Province, South Korea, known for its agricultural landscape and historical sites.
-
B.
Haeju
Haeju is a coastal city in southwestern North Korea, historically significant as a regional center and port on the Yellow Sea.
-
C.
Musan
Musan is a mining town in northeastern North Korea known for its large iron ore deposits and proximity to the Chinese border.
-
D.
Hajong
The Hajong are an indigenous ethnic group of northeastern India and neighboring Bangladesh, known for their Tibeto-Burman origins, agrarian lifestyle, and distinct language and cultural traditions.
-
E.
Komam-ni
Komam-ni is a village in South Korea known for being a key site of fighting during the Korean War’s Battle of Masan.
- 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_69e0b4c845e88190b4c5f3ae79291182 |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c226fbf881909794eff3ee9e206b |
completed | April 21, 2026, 12:17 a.m. |
Created at: April 16, 2026, 12:33 p.m.