Triple
T19881455
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gyeongchun Line |
E477782
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Gyeongseong (Seoul) |
—
|
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: Gyeongseong (Seoul) | Statement: [Gyeongchun Line, namedAfter, Gyeongseong (Seoul)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gyeongseong (Seoul) Context triple: [Gyeongchun Line, namedAfter, Gyeongseong (Seoul)]
-
A.
Jung-gu, Seoul
Jung-gu, Seoul is a central district of South Korea’s capital city, known for its major commercial areas, historic sites, and key government and business institutions.
-
B.
Seoul
chosen
Seoul is the capital and largest metropolis of South Korea, known as a major global center for technology, culture, and finance.
-
C.
Yongin
Yongin is a rapidly growing city in the Seoul Capital Area of South Korea, known for attractions like Everland Resort and the Korean Folk Village.
-
D.
Sejong City
Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
-
E.
Kangbuk-gu, Seoul
Kangbuk-gu, Seoul is a northern district of South Korea’s capital city known for its residential neighborhoods, mountainous landscapes, and cultural sites.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658df3f5c81909b5b290de91b8d50 |
completed | April 20, 2026, 4:48 p.m. |
Created at: April 10, 2026, 1:52 p.m.