Triple
T6563824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ulju-gun |
E153850
|
entity |
| Predicate | hasRomanization |
P2508
|
FINISHED |
| Object | Ulju-gun |
E153850
|
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: Ulju-gun | Statement: [Ulju-gun, hasRomanization, Ulju-gun]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ulju-gun Context triple: [Ulju-gun, hasRomanization, Ulju-gun]
-
A.
Ulju-gun
chosen
Ulju-gun is a county-level administrative district located within the metropolitan city of Ulsan in South Korea, known for its mix of industrial facilities and natural landscapes.
-
B.
Kakogawa
Kakogawa is an industrial and residential city in central Hyōgo Prefecture, Japan, known for its steel manufacturing and role as a regional transportation hub.
-
C.
Miura District
Miura District is a rural administrative district in Kanagawa Prefecture, Japan, known for its coastal towns and scenic Miura Peninsula landscapes.
-
D.
Kasukabe
Kasukabe is a city in Japan known for its suburban character within the Greater Tokyo area and as the setting of the popular manga and anime series "Crayon Shin-chan."
-
E.
Kikuchi
Kikuchi is a Japanese surname borne by various notable individuals across fields such as acting, sports, and academia.
- 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_69c6880cb35881909b763eb0125236b9 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6ae3a40488190892d20ca0d60b937 |
completed | March 27, 2026, 4:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6d5622e0481909b0ac0f4e06d19bc |
completed | March 27, 2026, 7:07 p.m. |
Created at: March 27, 2026, 1:52 p.m.