Triple
T9413081
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uiwang |
E226750
|
entity |
| Predicate | hasMountain |
P10602
|
FINISHED |
| Object | Cheonggyesan |
E797484
|
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: Cheonggyesan | Statement: [Uiwang, hasMountain, Cheonggyesan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Cheonggyesan Context triple: [Uiwang, hasMountain, Cheonggyesan]
-
A.
Ok-dong
Ok-dong is a neighborhood in Ulsan, South Korea, known for encompassing the large urban green space of Ulsan Grand Park.
-
B.
Geumjeongsan
Geumjeongsan is a prominent mountain in Busan, South Korea, known for its scenic hiking trails, historic fortress walls, and cultural sites.
-
C.
Baegunsan
chosen
Baegunsan is a mountain located in or near the city of Uiwang in South Korea, known for its hiking trails and natural scenery.
-
D.
Namsan
Namsan is a prominent central mountain in Seoul, South Korea, known for its panoramic city views and the iconic N Seoul Tower.
-
E.
Gyeryongsan
Gyeryongsan is a prominent mountain in central South Korea known for its scenic national park, rich biodiversity, and cultural sites including historic Buddhist temples.
- 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_69ca843280488190bc65600e843ef9e6 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd5258f7e081908d48600409181fdb |
completed | April 1, 2026, 5:14 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d11026b8688190b958ca65dae2d588 |
completed | April 4, 2026, 1:20 p.m. |
Created at: March 30, 2026, 7:47 p.m.