Triple
T1173754
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2018 Winter Olympics |
E24970
|
entity |
| Predicate | isInCity |
P12399
|
FINISHED |
| Object | Pyeongchang |
E133202
|
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: [2018 Winter Olympics, isInCity, Pyeongchang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pyeongchang Context triple: [2018 Winter Olympics, isInCity, Pyeongchang]
-
A.
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.
-
B.
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).
-
C.
Pyeongchang Mountain Cluster
chosen
Pyeongchang Mountain Cluster is the group of mountain-based venues in Pyeongchang, South Korea, that hosted most of the snow events during the 2018 Winter Olympics.
-
D.
Jinju, South Korea
Jinju, South Korea is a historic city in South Gyeongsang Province known for its riverside fortress, role in the Imjin War, and annual lantern festival.
-
E.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
- 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_69a494082a7c819095004f423f294a64 |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bcee38c881909c2fc73ba35f7253 |
completed | March 1, 2026, 10:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac93b6343c8190af6e28ccdaab6562 |
completed | March 7, 2026, 9:08 p.m. |
Created at: March 1, 2026, 7:45 p.m.