Triple
T6810245
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daejeon Metro |
E156610
|
entity |
| Predicate | usesTicketingSystem |
P1740
|
FINISHED |
| Object | T-money |
E467941
|
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: T-money | Statement: [Daejeon Metro, usesTicketingSystem, T-money]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: T-money Context triple: [Daejeon Metro, usesTicketingSystem, T-money]
-
A.
T-money card
chosen
The T-money card is a rechargeable contactless smart card widely used in South Korea for convenient payment of public transportation fares and small purchases.
-
B.
T-Money
T-Money is a television personality and DJ best known for co-hosting the influential hip-hop music video show "Yo! MTV Raps."
-
C.
Busan Metro
Busan Metro is the rapid transit system serving the city of Busan, South Korea, providing extensive urban and suburban rail transportation across the metropolitan area.
-
D.
Daejeon Metro
Daejeon Metro is the urban rapid transit system serving the city of Daejeon in South Korea.
-
E.
Gwangju Metro
Gwangju Metro is the urban rapid transit system serving the city of Gwangju in South Korea.
- 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_69c68828b26c819090fe9df7612bbc27 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d30ded6481908fd64611607c610e |
completed | March 27, 2026, 6:57 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c71aa7dc3c81909ef422b5c51ae6be |
completed | March 28, 2026, 12:02 a.m. |
Created at: March 27, 2026, 2:16 p.m.