Triple
T881122
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Saab 96 |
E19028
|
entity |
| Predicate | designer |
P184
|
FINISHED |
| Object | Sixten Sason |
E97886
|
NE FINISHED |
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: [Saab 96, designer, Sixten Sason]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sixten Sason Context triple: [Saab 96, designer, 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.
Vilailuck Teigen
Vilailuck Teigen is a Thai-American television personality and social media figure best known as the mother of model and author Chrissy Teigen.
-
C.
Lyness
Lyness is a small coastal village and former naval base on the island of Hoy in Orkney, Scotland.
-
D.
Sommerda
Sommerda is a town in the German state of Thuringia, known for its industrial history and central location near the Unstrut River.
-
E.
Kasha Kropinski
Kasha Kropinski is a South African-born actress best known for her role as Ruth Cole on the American Western television series "Hell on Wheels."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a4939c32488190a7ccd41cf0abb22b |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4accb653c81909fe0753f78145be9 |
completed | March 1, 2026, 9:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a7b85883c481909261bde7fdebcde1 |
completed | March 4, 2026, 4:43 a.m. |
Created at: March 1, 2026, 7:39 p.m.