Triple
T7978703
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Taejongdae Park |
E185511
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Yeongdo-gu |
E34692
|
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: Yeongdo-gu | Statement: [Taejongdae Park, locatedIn, Yeongdo-gu]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Yeongdo-gu Context triple: [Taejongdae Park, locatedIn, Yeongdo-gu]
-
A.
Yeongdo District
chosen
Yeongdo District is a coastal district of Busan, South Korea, known for its island setting, shipbuilding industry, and scenic views of the city and harbor.
-
B.
Kangseo-gu
Kangseo-gu is the romanized name of Gangseo District, an administrative district of Seoul, South Korea.
-
C.
Gyeyang-gu
Gyeyang-gu is an administrative district of Incheon, South Korea, known for its mix of residential areas, historical sites, and access to natural attractions like Gyeyang Mountain.
-
D.
Yeonsu-gu
Yeonsu-gu is an administrative district of Incheon, South Korea, known for its coastal location, modern residential areas, and proximity to the Songdo International Business District.
-
E.
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.
- 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_69ca829851908190b4e03829353ee7c3 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3bf84b1081908e60a556d984aad6 |
completed | March 31, 2026, 3:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce8828faf48190927b2a6680f6b4d8 |
completed | April 2, 2026, 3:15 p.m. |
Created at: March 30, 2026, 5:14 p.m.