Triple
T23238604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jørgen Løvland |
E581372
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jørgen |
—
|
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: Jørgen | Statement: [Jørgen Løvland, givenName, Jørgen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jørgen Context triple: [Jørgen Løvland, givenName, Jørgen]
-
A.
Jørgen
chosen
Jørgen is a Scandinavian male given name, commonly used in Denmark and Norway and related to the name George.
-
B.
Søren
Søren is a masculine given name of Scandinavian origin, most famously borne by the Danish philosopher Søren Kierkegaard.
-
C.
Mads Jurik
Mads Jurik is a cryptographer known for his work on public-key cryptosystems and contributions to theoretical computer science, often in collaboration with Ivan Damgård.
-
D.
Bjørn
Bjørn is a Scandinavian male given name, commonly used in Norway and Denmark and meaning "bear."
-
E.
Erik Jørgensen
Erik Jørgensen was a Norwegian firearms designer best known for co-developing the Krag–Jørgensen bolt-action rifle used by several national armies in the late 19th and early 20th centuries.
- 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_69e2460556f88190be1744a84a84173f |
completed | April 17, 2026, 2:39 p.m. |
| NER | Named-entity recognition | batch_69f192ebaef4819083a7805537ad993f |
completed | April 29, 2026, 5:11 a.m. |
Created at: April 17, 2026, 4:10 p.m.