Triple
T3269040
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Carine |
E68597
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Karine |
E68597
|
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: Karine | Statement: [Carine, hasVariant, Karine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karine Context triple: [Carine, hasVariant, Karine]
-
A.
Carine
chosen
Carine is a feminine given name, often considered a variant of names like Catherine or Karine, used in various European languages.
-
B.
Karin
Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
-
C.
Erika
Erika is a feminine given name of German origin, borne by numerous notable figures including writer and actress Erika Mann.
-
D.
Kaarina
Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
-
E.
Nina
Nina is a Danish fashion model best known for her appearances in the Sports Illustrated Swimsuit Issue and various high-profile advertising campaigns.
- 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_69ad859b54f881909bf530d549caf2fd |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adafd0eddc8190834a64f6b8e8e9f9 |
completed | March 8, 2026, 5:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b28efded588190bd6c361e5298b496 |
completed | March 12, 2026, 10:01 a.m. |
Created at: March 8, 2026, 3:09 p.m.