Triple
T19881828
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namsan |
E477792
|
entity |
| Predicate | belongsTo |
P35
|
FINISHED |
| Object | Seoul Special City |
—
|
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: Seoul Special City | Statement: [Namsan, belongsTo, Seoul Special City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Seoul Special City Context triple: [Namsan, belongsTo, Seoul Special City]
-
A.
Seoul Special City
chosen
Seoul Special City is the capital and largest metropolis of South Korea, serving as the country’s political, economic, and cultural center.
-
B.
Seoul Land
Seoul Land is a major amusement park in Gwacheon, South Korea, featuring a variety of rides, themed zones, and family-friendly attractions.
-
C.
Poseuko Taueo Seoul
Poseuko Taueo Seoul is the Korean romanized name for Posco Tower Seoul, a prominent skyscraper and office building in Seoul, South Korea.
-
D.
Itaewon
Itaewon is a vibrant multicultural district in Seoul known for its international cuisine, nightlife, and diverse expatriate community.
-
E.
Itaewon Station
Itaewon Station is a Seoul Metropolitan Subway station serving the popular Itaewon neighborhood, known for its international dining, nightlife, and shopping.
- 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.