Triple
T7301463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Han River |
E167863
|
entity |
| Predicate | passesNear |
P416
|
FINISHED |
| Object | Jamsil |
E566377
|
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: Jamsil | Statement: [Han River, passesNear, Jamsil]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jamsil Context triple: [Han River, passesNear, Jamsil]
-
A.
Jamsil
chosen
Jamsil is a neighborhood in southeastern Seoul, South Korea, known for its major sports complexes, large residential areas, and entertainment facilities such as Lotte World.
-
B.
Koung-Khi
Koung-Khi is an administrative department located in the West Region of Cameroon.
-
C.
Anseongcheon
Anseongcheon is a river in South Korea that flows through the city of Pyeongtaek in Gyeonggi Province.
-
D.
Seogwipo
Seogwipo is a coastal city on South Korea’s Jeju Island known for its waterfalls, volcanic landscapes, and popular tourist attractions.
-
E.
Hwaseong
Hwaseong is a city in Gyeonggi Province, South Korea, known for its rapid industrial growth and proximity to major urban centers like Suwon and Seoul.
- 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_69c6888c820881909fc68f689fe1c251 |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6ebb09164819099c4479d48c1688a |
completed | March 27, 2026, 8:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7eee71f5c8190aaff605eeff07390 |
completed | March 28, 2026, 3:08 p.m. |
Created at: March 27, 2026, 3:01 p.m.