Triple
T22313193
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Erling Bjørnson |
E551573
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Erling |
—
|
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: Erling | Statement: [Erling Bjørnson, givenName, Erling]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Erling Context triple: [Erling Bjørnson, givenName, Erling]
-
A.
Erling
chosen
Erling is a masculine given name of Scandinavian origin, commonly used in Norway and other Nordic countries.
-
B.
Eirik
Eirik is a given name, primarily used in Scandinavian countries, that is a variant of the name Eric.
-
C.
Torbjørn
Torbjørn is a Scandinavian masculine given name, particularly common in Norway, derived from Old Norse elements meaning "Thor" and "bear."
-
D.
Svein
Svein is a masculine given name of Old Norse origin, commonly used in Scandinavian countries.
-
E.
Geir
Geir is a masculine given name of Old Norse origin, commonly used in Scandinavian countries and meaning "spear."
- 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_69e11e4776588190abb21e5cea79973f |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15750f76c81909d6f788928f503f1 |
completed | April 29, 2026, 12:56 a.m. |
Created at: April 16, 2026, 8:42 p.m.