Triple
T21721023
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inwangsan |
E536154
|
entity |
| Predicate | cityViewpointFor |
P100951
|
FINISHED |
| Object | Bukhansan |
—
|
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: Bukhansan | Statement: [Inwangsan, cityViewpointFor, Bukhansan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bukhansan Context triple: [Inwangsan, cityViewpointFor, Bukhansan]
-
A.
Baegunsan
Baegunsan is a mountain located in or near the city of Uiwang in South Korea, known for its hiking trails and natural scenery.
-
B.
Geumjeongsan
Geumjeongsan is a prominent mountain in Busan, South Korea, known for its scenic hiking trails, historic fortress walls, and cultural sites.
-
C.
Baegaksan
chosen
Baegaksan is a prominent mountain in central Seoul, South Korea, known for its historical significance as part of the city's protective fortress wall and its scenic hiking trails overlooking the capital.
-
D.
Gwanggyo Mountain
Gwanggyo Mountain is a prominent natural landmark in South Korea known for its hiking trails and scenic views near the city of Suwon.
-
E.
Ok-dong
Ok-dong is a neighborhood in Ulsan, South Korea, known for encompassing the large urban green space of Ulsan Grand Park.
- 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_69e0c46c6dd88190a595375fa6ebd701 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69efd96f1fbc8190a202f834aec1a319 |
completed | April 27, 2026, 9:47 p.m. |
Created at: April 16, 2026, 6:47 p.m.