Triple
T5569257
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Soohorang |
E145955
|
entity |
| Predicate | associatedWithCity |
P1481
|
FINISHED |
| Object | Pyeongchang |
E413371
|
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: Pyeongchang | Statement: [Soohorang, associatedWithCity, Pyeongchang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pyeongchang Context triple: [Soohorang, associatedWithCity, Pyeongchang]
-
A.
Pyeongchang
chosen
Pyeongchang is a county in South Korea best known internationally for hosting the 2018 Winter Olympics.
-
B.
Gangneung
Gangneung is a coastal city in South Korea’s Gangwon Province, known for its beaches, cultural festivals, and role as a host city during the 2018 Pyeongchang Winter Olympics.
-
C.
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.
-
D.
Neryungri
Neryungri is a major coal-mining and industrial city in southeastern Siberia, Russia, known as one of the key urban centers of the Sakha Republic (Yakutia).
-
E.
Daegu, South Korea
Daegu, South Korea is a major city in the southeastern part of the country known for its role as an industrial, cultural, and educational center.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0204f0d288190b9d4884665ba9116 |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c028485ba081908565f7f852278cfc |
completed | March 22, 2026, 5:35 p.m. |
Created at: March 22, 2026, 3:36 p.m.