Triple
T8277877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gojong of Korea |
E193590
|
entity |
| Predicate | capitalDuringReign |
P9119
|
FINISHED |
| Object | Han-seong |
E723127
|
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: Han-seong | Statement: [Gojong of Korea, capitalDuringReign, Han-seong]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Han-seong Context triple: [Gojong of Korea, capitalDuringReign, Han-seong]
-
A.
Han-seong
chosen
Han-seong was the historical name for Korea’s capital city, now known as Seoul, which served as the political and cultural center of the Joseon dynasty.
-
B.
Seonghwan
Seonghwan is a locality in South Korea historically noted as the site of the Battle of Seonghwan during the First Sino-Japanese War.
-
C.
Jinhae
Jinhae is a coastal district in Changwon, South Korea, best known for its large naval base and famous annual cherry blossom festival.
-
D.
Lee Tae-hun
Lee Tae-hun is a South Korean film producer known for his work on the international release of the science fiction thriller "Snowpiercer."
-
E.
Jinwicheon
Jinwicheon is a river flowing through the city of Pyeongtaek in South Korea.
- 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_69ca82e217a48190880695635c44b2ed |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb79ebb6b88190bc777b8bd72fcdbc |
completed | March 31, 2026, 7:38 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd951c06b88190962b108b3325d30b |
completed | April 1, 2026, 9:58 p.m. |
Created at: March 30, 2026, 5:51 p.m.