Triple
T22262557
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolf Eliassen |
E550264
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rolf |
—
|
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: Rolf | Statement: [Rolf Eliassen, givenName, Rolf]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rolf Context triple: [Rolf Eliassen, givenName, Rolf]
-
A.
Rolf
chosen
Rolf is a masculine given name of Germanic origin commonly used in German-speaking and Scandinavian countries.
-
B.
Rolph
Rolph is a surname most notably associated with James Rolph, a prominent early 20th-century American politician and former mayor of San Francisco and governor of California.
-
C.
Ralf
Ralf is a masculine given name of German origin commonly used in German-speaking and Scandinavian countries.
-
D.
Roal
Roal is a small settlement located in the Hadeland district of southeastern Norway.
-
E.
Mel Wulf
Mel Wulf was a prominent American civil liberties lawyer who served as legal director of the ACLU and was known for his work on landmark constitutional and free speech cases.
- 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_69e11e42adb8819087714772ea606709 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f141b94a688190b17c55477993a745 |
completed | April 28, 2026, 11:24 p.m. |
Created at: April 16, 2026, 8:39 p.m.