Triple
T20101380
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Karin Stoltenberg |
E496549
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Karin |
—
|
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: Karin | Statement: [Karin Stoltenberg, givenName, Karin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Karin Context triple: [Karin Stoltenberg, givenName, Karin]
-
A.
Karin
Karin is the historical name of the city now known as Erzurum, a major urban center in eastern Anatolia with a long and strategic past.
-
B.
Karin
chosen
Karin is a feminine given name used in various cultures, often considered a variant of names like Karen or Katherine.
-
C.
Karin
Karin is the main settlement and administrative center of Abemama Atoll in Kiribati.
-
D.
Kaarina
Kaarina is a town and municipality in southwestern Finland, located near the city of Turku.
-
E.
Karina
Karina is a retired Canadian soccer goalkeeper and Olympic bronze medalist who played for the Canadian women’s national team.
- 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_69da626eee3881909f3454986d4a6511 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e6666ff4008190ae1eec907c89bd3b |
completed | April 20, 2026, 5:46 p.m. |
Created at: April 11, 2026, 11:27 p.m.