Triple
T7194782
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dong-gu, Daejeon |
E168584
|
entity |
| Predicate | hasRomanization |
P2508
|
FINISHED |
| Object | Dong-gu |
E168584
|
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: Dong-gu | Statement: [Dong-gu, Daejeon, hasRomanization, Dong-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dong-gu Context triple: [Dong-gu, Daejeon, hasRomanization, Dong-gu]
-
A.
Dong-gu
Dong-gu is an administrative district of the metropolitan city of Ulsan in South Korea, known for its coastal location and industrial facilities.
-
B.
Dong-gu
Dong-gu is an administrative district in the city of Daegu, South Korea, known for its mix of urban neighborhoods and surrounding natural landscapes.
-
C.
Dong-gu
chosen
Dong-gu is a district-level administrative area within the metropolitan city of Daejeon in South Korea.
-
D.
Jung-gu
Jung-gu is a central district of the metropolitan city of Daejeon in South Korea, known for its mix of commercial, residential, and administrative areas.
-
E.
Jung-gu
Jung-gu is a central administrative district of the metropolitan city of Ulsan 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_69c68a5376748190bb500f03df86e93e |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6e9050164819081fd6a11d10f9833 |
completed | March 27, 2026, 8:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c9765b3c5081908c3114271b5d3e15 |
completed | March 29, 2026, 6:58 p.m. |
Created at: March 27, 2026, 2:51 p.m.