Triple
T2875849
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kia Nurse |
E56875
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Kia |
E271033
|
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: Kia | Statement: [Kia Nurse, givenName, Kia]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kia Context triple: [Kia Nurse, givenName, Kia]
-
A.
Kia
chosen
Kia is a feminine given name used in various cultures, often as a short form of names like Kiara or Kiana.
-
B.
Kia Motors
Kia Motors is a South Korean automobile manufacturer known for producing a wide range of affordable cars and SUVs for global markets.
-
C.
Hyundai Motor Company
Hyundai Motor Company is a leading South Korean automotive manufacturer known globally for producing a wide range of affordable and reliable vehicles.
-
D.
Kia Optima
The Kia Optima is a midsize family sedan known for its value, modern styling, and generous features.
-
E.
Hyundai Sonata
The Hyundai Sonata is a popular mid-size family sedan known for its value, modern styling, and extensive features.
- 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_69ab4a4ced288190ab6d3e062d10f7f6 |
completed | March 6, 2026, 9:42 p.m. |
| NER | Named-entity recognition | batch_69abe0061d048190bb1e5a01e7ceb0e2 |
completed | March 7, 2026, 8:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b01dbb28588190ba2daae192744908 |
completed | March 10, 2026, 1:33 p.m. |
Created at: March 6, 2026, 10:03 p.m.