Triple
T9266984
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Olympic Games mascots |
E222724
|
entity |
| Predicate | notableExample |
P1503
|
FINISHED |
| Object | Soohorang |
E145955
|
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: Soohorang | Statement: [Olympic Games mascots, notableExample, Soohorang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Soohorang Context triple: [Olympic Games mascots, notableExample, Soohorang]
-
A.
Soohorang
chosen
Soohorang is the white tiger character that served as the official mascot of the 2018 Winter Olympics in Pyeongchang, South Korea.
-
B.
Yo-jong
Yo-jong is a North Korean political figure best known as the influential sister of leader Kim Jong-un and a senior official in the ruling Workers’ Party of Korea.
-
C.
Jinwicheon
Jinwicheon is a river flowing through the city of Pyeongtaek in South Korea.
-
D.
Sojin
Sojin is a given name, often used in East Asian cultures, that can refer to various individuals in entertainment, arts, and other fields.
-
E.
Seonghwan
Seonghwan is a locality in South Korea historically noted as the site of the Battle of Seonghwan during the First Sino-Japanese War.
- 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd074bac9481909419988a9e8d9bd5 |
completed | April 1, 2026, 11:53 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d09c193b548190afe79d0c84fa2bd3 |
completed | April 4, 2026, 5:05 a.m. |
Created at: March 30, 2026, 7:33 p.m.