Triple
T13054414
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jinsen Arsenal |
E327532
|
entity |
| Predicate | location |
P40
|
FINISHED |
| Object | Incheon |
E27787
|
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: Incheon | Statement: [Jinsen Arsenal, location, Incheon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Incheon Context triple: [Jinsen Arsenal, location, Incheon]
-
A.
Incheon
chosen
Incheon is a major port city in northwestern South Korea, known for its international airport and role as a key transportation and economic hub.
-
B.
Daegu
Daegu is a major metropolitan city in southeastern South Korea known for its textile industry, electronics manufacturing, and cultural festivals.
-
C.
Daejeon
Daejeon is a major city in central South Korea known as a hub for science, technology, and research institutions.
-
D.
Ulsan
Ulsan is a major industrial city in southeastern South Korea, known for its large automobile, shipbuilding, and petrochemical complexes.
-
E.
Busan
Busan is South Korea’s second-largest city and a major international port known for its bustling harbor, beaches, and coastal scenery.
- 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_69d8076e64308190904fb5c93517c901 |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69d980bb52d88190b5be12000e27a2c9 |
completed | April 10, 2026, 10:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf0683b90819098864f73b6976517 |
completed | May 8, 2026, 2:17 p.m. |
Created at: April 9, 2026, 8:58 p.m.