Triple
T6508747
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Expo 93 |
E150074
|
entity |
| Predicate | hostCityRegion |
P28660
|
FINISHED |
| Object | Daejeon Metropolitan City |
E28250
|
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: Daejeon Metropolitan City | Statement: [Expo 93, hostCityRegion, Daejeon Metropolitan City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Daejeon Metropolitan City Context triple: [Expo 93, hostCityRegion, Daejeon Metropolitan City]
-
A.
Daejeon
chosen
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
B.
Sejong City
Sejong City is South Korea’s planned administrative capital, designed to house numerous government ministries and ease congestion in Seoul.
-
C.
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.
-
D.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
E.
Uijeongbu
Uijeongbu is a city in South Korea known as a suburban hub north of Seoul, featuring residential districts, commercial centers, and a history of hosting U.S. military bases.
- 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_69c687ef291081909d437f035eef1cda |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c69f386aa08190bfc8592a92ec6339 |
completed | March 27, 2026, 3:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7bf66c0308190a09736eafe61c966 |
completed | March 28, 2026, 11:45 a.m. |
Created at: March 27, 2026, 1:43 p.m.