Triple
T22552280
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Klaus Samelson |
E557587
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Samelson |
—
|
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: Samelson | Statement: [Klaus Samelson, familyName, Samelson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Samelson Context triple: [Klaus Samelson, familyName, Samelson]
-
A.
Samelson
chosen
Samelson is a German surname most notably associated with Klaus Samelson, a pioneering computer scientist and mathematician.
-
B.
Casselman
Casselman is a small bilingual village and municipality in Eastern Ontario, Canada, known for its francophone community and location along the South Nation River.
-
C.
Hamel
Hamel is a surname of French origin borne by various notable individuals across fields such as entertainment, sports, and academia.
-
D.
Mackey
Mackey is a surname most prominently associated with John Mackey, the co-founder and longtime CEO of Whole Foods Market.
-
E.
Semmes
Semmes is a surname most notably associated with Raphael Semmes, a Confederate naval officer and captain of the commerce raider CSS Alabama during the American Civil War.
- 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_69e11e59db848190b4272ecd2b690ffd |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f15f7647208190a1aaebd083bf095a |
completed | April 29, 2026, 1:31 a.m. |
Created at: April 16, 2026, 8:52 p.m.