Triple
T22265719
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | W Korea |
E550344
|
entity |
| Predicate | publisher |
P29
|
FINISHED |
| Object | Doosan Magazine |
—
|
NE NERFINISHED |
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: Doosan Magazine | Statement: [W Korea, publisher, Doosan Magazine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Doosan Magazine Context triple: [W Korea, publisher, Doosan Magazine]
-
A.
Doosan Magazine
chosen
Doosan Magazine is a South Korean publishing company best known for producing prominent fashion and lifestyle titles, including Vogue Korea.
-
B.
Doosan DST
Doosan DST is a South Korean defense company known for developing and producing advanced military systems, including armored vehicles and weapons platforms.
-
C.
Doosan
Doosan is a South Korean multinational conglomerate best known for its heavy industries, construction equipment, and engineering businesses.
-
D.
Daewoo Precision Industries
Daewoo Precision Industries is a South Korean firearms manufacturer known for producing military small arms and service rifles.
-
E.
POSCO DX
POSCO DX is a South Korean industrial digital solutions and engineering company specializing in smart factory, automation, and IT services, particularly for the steel and manufacturing sectors.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e11e43d8208190aff4f9cf7f2c2a8a |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141bb850881908f5e9c37afb52ca8 |
completed | April 28, 2026, 11:24 p.m. |
Created at: April 16, 2026, 8:39 p.m.