Triple
T19881794
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Namsan |
E477792
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object | Namsan Seoul Tower |
E532617
|
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: Namsan Seoul Tower | Statement: [Namsan, hasLandmark, Namsan Seoul Tower]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Namsan Seoul Tower Context triple: [Namsan, hasLandmark, Namsan Seoul Tower]
-
A.
N Seoul Tower
chosen
N Seoul Tower is a prominent communication and observation tower on Namsan Mountain that serves as one of Seoul’s most recognizable cityscape landmarks and tourist attractions.
-
B.
Mount Namsan
Mount Namsan is a historically significant mountain in Gyeongju, South Korea, renowned for its numerous ancient Buddhist relics, temples, and archaeological sites.
-
C.
Kyobo Tower, Seoul
Kyobo Tower in Seoul is a prominent modern office and commercial building best known as a landmark work of Swiss architect Mario Botta.
-
D.
Busan Tower
Busan Tower is a prominent observation tower in Busan, South Korea, offering panoramic views of the city and its harbor.
-
E.
Samseongsan
Samseongsan is a mountain in South Korea known for its hiking trails and views over the Anyang and southern Seoul metropolitan area.
- 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_69d8e51f32b08190b3687f4f60353250 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e658df3f5c81909b5b290de91b8d50 |
completed | April 20, 2026, 4:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07ea3c3688819097ec23445a32e875 |
completed | May 16, 2026, 3:53 a.m. |
Created at: April 10, 2026, 1:52 p.m.