Triple
T6508673
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Expo Science Park |
E150073
|
entity |
| Predicate | locatedOnRiver |
P165
|
FINISHED |
| Object | Gapcheon |
E152147
|
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: Gapcheon | Statement: [Expo Science Park, locatedOnRiver, Gapcheon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gapcheon Context triple: [Expo Science Park, locatedOnRiver, Gapcheon]
-
A.
Gapcheon
chosen
Gapcheon is a major river flowing through the city of Daejeon in South Korea, serving as a central natural and recreational landmark.
-
B.
Pocheon
Pocheon is a city in northeastern South Korea known for its natural scenery, including mountains, lakes, and recreational forests, within Gyeonggi Province.
-
C.
Mokneung
Mokneung is one of the royal burial sites from Korea’s Joseon Dynasty, forming part of the UNESCO-listed Royal Tombs complex.
-
D.
Gwangmyeong
Gwangmyeong is a city in South Korea known for its proximity to Seoul and attractions like the Gwangmyeong Cave, a former mine turned cultural and tourism complex.
-
E.
Yeoju
Yeoju is a city in South Korea known for its rich historical heritage, including royal tombs and ceramics, and its scenic riverside landscapes.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f386aa08190bfc8592a92ec6339 |
completed | March 27, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6cb5782fc8190a56b714bbc007490 |
completed | March 27, 2026, 6:24 p.m. |
Created at: March 27, 2026, 1:43 p.m.