Triple
T17744691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Peer Jacob Svenkerud |
E442956
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Peer Jacob |
—
|
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: Peer Jacob | Statement: [Peer Jacob Svenkerud, givenName, Peer Jacob]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peer Jacob Context triple: [Peer Jacob Svenkerud, givenName, Peer Jacob]
-
A.
Jaccob
Jaccob is a masculine given name most notably borne by American professional ice hockey defenseman Jaccob Slavin.
-
B.
Peer Jacob Svenkerud
chosen
Peer Jacob Svenkerud is a Norwegian academic and university leader who serves as rector of the Inland Norway University of Applied Sciences.
-
C.
Jakob
Jakob is the given name of Johann Jakob Kaup, a 19th-century German naturalist and zoologist known for his work in classifying vertebrates.
-
D.
Jakob Grayson
Jakob Grayson is a fictional character from Joe Hill’s post-apocalyptic novel "The Fireman," set in a world ravaged by a deadly spore that causes spontaneous combustion.
-
E.
Jacob's
Jacob's is a well-known biscuit brand originating from Ireland, recognized for products like cream crackers and a variety of sweet and savory biscuits.
- 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_69d8b9ed3a2081909b2ec0d4dd2f4c37 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e47ad0c5b481909059bfa868cc4001 |
completed | April 19, 2026, 6:48 a.m. |
Created at: April 10, 2026, 10:09 a.m.