Triple
T18138838
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sason |
E434207
|
entity |
| Predicate | notableBearer |
P458
|
FINISHED |
| Object | Sixten Sason |
—
|
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: Sixten Sason | Statement: [Sason, notableBearer, Sixten Sason]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sixten Sason Context triple: [Sason, notableBearer, Sixten Sason]
-
A.
Sixten Sason
chosen
Sixten Sason was a pioneering Swedish industrial designer best known for shaping Saab’s early automobiles and helping define the brand’s distinctive aerodynamic style.
-
B.
Sixten
Sixten is a masculine given name of Scandinavian origin, most notably borne by Swedish industrial designer Sixten Sason.
-
C.
Sattler
Sattler is a German-origin surname borne by various notable individuals across fields such as politics, science, and the arts.
-
D.
Sakshaug
Sakshaug is a village in the municipality of Inderøy in Trøndelag county, Norway, known for its historic church and rural setting.
-
E.
Sanna
Sanna is a river in the Tyrol region of western Austria, known as a tributary of the Inn and a popular destination for whitewater sports.
- 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_69d8b90aac308190801e2c57d8c5bfe5 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4de0993e88190b19c5cb35a6d252d |
completed | April 19, 2026, 1:52 p.m. |
Created at: April 10, 2026, 10:29 a.m.