Triple
T493852
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Governor-General of Korea |
E10246
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Keijō |
E19209
|
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: Keijō | Statement: [Governor-General of Korea, location, Keijō]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keijō Context triple: [Governor-General of Korea, location, Keijō]
-
A.
Nara
Nara is an ancient Japanese city renowned for its early role as a national capital, its historic temples, and its culturally significant deer-filled parks.
-
B.
Pyongyang
Pyongyang is the capital and largest city of North Korea, serving as its political, economic, and cultural center.
-
C.
Seoul
chosen
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
D.
Sendai
Sendai is the largest city in Japan’s Tōhoku region, known for its lush greenery, historic sites, and status as a major economic and cultural center in northeastern Honshu.
-
E.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
- 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_69a2e847df8481909239ec08ccf1e376 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2f0fbfa408190aeb3b93996a35c00 |
completed | Feb. 28, 2026, 1:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a4ed30d578819091c6c1f4c5eba301 |
completed | March 2, 2026, 1:51 a.m. |
Created at: Feb. 28, 2026, 1:12 p.m.