Triple
T10567358
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pusanjin-gu |
E249383
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Dongnae-gu |
E34836
|
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: Dongnae-gu | Statement: [Pusanjin-gu, locatedNear, Dongnae-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dongnae-gu Context triple: [Pusanjin-gu, locatedNear, Dongnae-gu]
-
A.
Dongnae District
chosen
Dongnae District is a historic and central administrative district of Busan, South Korea, known for its hot springs and cultural heritage sites.
-
B.
Dongan-gu
Dongan-gu is an urban district of Anyang in Gyeonggi Province, South Korea, known for its residential neighborhoods, commercial centers, and proximity to Seoul.
-
C.
Pusanjin-gu
Pusanjin-gu is a central urban district of Busan, South Korea, known for its major commercial areas, transportation hubs, and dense residential neighborhoods.
-
D.
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.
-
E.
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.
- 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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d5272ef5848190b76d671ea2d26314 |
completed | April 7, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e6035cf86081909603cec9aa5bd9d6 |
completed | April 20, 2026, 10:43 a.m. |
Created at: April 6, 2026, 12:36 p.m.