Triple
T7386874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sauter |
E170402
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Sautter |
E174891
|
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: Sautter | Statement: [Sauter, hasVariant, Sautter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sautter Context triple: [Sauter, hasVariant, Sautter]
-
A.
Sautter
chosen
Sautter is a surname, likely a spelling variant of "Sutter," borne by various individuals and families of European origin.
-
B.
Sauter
Sauter is a surname of German origin, often associated with individuals in fields such as music, engineering, and business.
-
C.
Nantz
Nantz is the surname of Jim Nantz, a prominent American sportscaster best known for his long-running work with CBS Sports covering events like the NFL, NCAA basketball, and The Masters.
-
D.
Sauvestre
Sauvestre is a French surname most notably associated with architect Stephen Sauvestre, who contributed to the design of the Eiffel Tower.
-
E.
Suhre
The Suhre is a river in Switzerland that flows through the cantons of Lucerne and Aargau before joining the Aare.
- 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_69c68a5e2c9081909e713ce866e0060a |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f1f2bac481908ac74069182a4ce4 |
completed | March 27, 2026, 9:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c86130228c819098544e5354c31b44 |
completed | March 28, 2026, 11:16 p.m. |
Created at: March 27, 2026, 3:08 p.m.