Triple
T9413127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hanam |
E226751
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object | Gwangju, Gyeonggi |
E443317
|
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: Gwangju, Gyeonggi | Statement: [Hanam, borderedBy, Gwangju, Gyeonggi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gwangju, Gyeonggi Context triple: [Hanam, borderedBy, Gwangju, Gyeonggi]
-
A.
Gwangju, Gyeonggi
chosen
Gwangju, Gyeonggi is a city in South Korea known for its blend of suburban residential areas, light industry, and historical sites within the Seoul Capital Area.
-
B.
Dongducheon
Dongducheon is a city in northern South Korea known for its proximity to the Demilitarized Zone and the presence of U.S. military bases.
-
C.
Namyangju
Namyangju is a city in South Korea known for its scenic natural landscapes, historical sites, and role as a suburban area within the Seoul metropolitan region.
-
D.
Pyeongtaek
Pyeongtaek is a South Korean city in Gyeonggi Province known for its major U.S. and UN military presence, including large bases such as Camp Humphreys.
-
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_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.