Triple
T19289373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taereung and Gangneung |
E482399
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Taereung |
—
|
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: Taereung | Statement: [Taereung and Gangneung, hasPart, Taereung]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taereung Context triple: [Taereung and Gangneung, hasPart, Taereung]
-
A.
Taebong
Taebong was a short-lived Korean kingdom of the early 10th century that emerged during the Later Three Kingdoms period before being absorbed by Goryeo.
-
B.
Namdaecheon
Namdaecheon is a river in Gangneung, South Korea, known for flowing through the city toward the East Sea and serving as a local natural and recreational landmark.
-
C.
Donggureung
chosen
Donggureung is a large royal burial complex in Guri, South Korea, containing multiple tombs of Joseon Dynasty kings and queens and recognized as part of a UNESCO World Heritage site.
-
D.
Haan-dong
Haan-dong is a neighborhood in the city of Gwangmyeong, South Korea, known as one of its local residential and commercial districts.
-
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 (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_69d8e8cf61b0819096fe3e4107827c4e |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fc050a888190ac204d1e736200c5 |
completed | April 20, 2026, 10:12 a.m. |
Created at: April 10, 2026, 1:30 p.m.