Triple
T18064807
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GM Daewoo |
E432268
|
entity |
| Predicate | successor |
P78
|
FINISHED |
| Object | GM Korea |
—
|
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: GM Korea | Statement: [GM Daewoo, successor, GM Korea]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GM Korea Context triple: [GM Daewoo, successor, GM Korea]
-
A.
GM Korea
chosen
GM Korea is the South Korean subsidiary of General Motors, engaged in the manufacturing and sale of automobiles for domestic and global markets.
-
B.
BMG Korea
BMG Korea was the South Korean branch of the global music company Bertelsmann Music Group, responsible for producing and distributing music in the Korean market.
-
C.
Gumi, South Korea
Gumi, South Korea is an industrial city in North Gyeongsang Province known as a major electronics manufacturing hub and home to several large technology companies.
-
D.
GTG Entertainment
GTG Entertainment was a television production company co-founded by influential TV executive Grant Tinker after his tenure at NBC.
-
E.
GMK
GMK is a Nigerian music producer known for crafting contemporary Afrobeats and hip-hop sounds, including work on Burna Boy’s “African Giant” era.
- 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_69d8b9070cac81909fa9473fb1c3f1c7 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4cce74a0c81908375e3100c7578b8 |
completed | April 19, 2026, 12:39 p.m. |
Created at: April 10, 2026, 10:26 a.m.