Triple
T20246066
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 2006 Paris Motor Show |
E498426
|
entity |
| Predicate | hasExhibitor |
P33707
|
FINISHED |
| Object | Kia |
—
|
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: Kia | Statement: [2006 Paris Motor Show, hasExhibitor, Kia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kia Context triple: [2006 Paris Motor Show, hasExhibitor, Kia]
-
A.
Kia
Kia is a feminine given name used in various cultures, often as a short form of names like Kiara or Kiana.
-
B.
Kia
Kia is a small settlement located on Santa Isabel Island in the Solomon Islands, known for its remote, rural character and coastal setting.
-
C.
KIA
KIA is the armed wing of the Kachin Independence Organization, a major ethnic insurgent group operating primarily in northern Myanmar’s Kachin State.
-
D.
Kia Motors
chosen
Kia Motors is a South Korean automobile manufacturer known for producing a wide range of affordable cars and SUVs for global markets.
-
E.
SsangYong Motor
SsangYong Motor is a South Korean automobile manufacturer best known for producing SUVs and off-road vehicles.
- 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_69da6274c58c81909c646eabed6f4f30 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e673a26c8481908f408fb6426aece9 |
completed | April 20, 2026, 6:42 p.m. |
Created at: April 11, 2026, 11:40 p.m.