Triple
T9672955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthias Zurbriggen |
E234073
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Zurbriggen |
E234073
|
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: Zurbriggen | Statement: [Matthias Zurbriggen, familyName, Zurbriggen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Zurbriggen Context triple: [Matthias Zurbriggen, familyName, Zurbriggen]
-
A.
Zurbriggen
chosen
Zurbriggen is a Swiss surname notably associated with mountaineers and alpine skiers.
-
B.
Wehrli
Wehrli is a surname most notably associated with Roger Wehrli, a Hall of Fame American football cornerback who played for the St. Louis Cardinals.
-
C.
Zurer
Zurer is the surname of Ayelet Zurer, an Israeli actress known for her roles in international films and television series.
-
D.
Gantenbein
Gantenbein is the enigmatic, shape-shifting central figure in Max Frisch’s novel "Mein Name sei Gantenbein," through whom themes of identity, role-playing, and the fluidity of self are explored.
-
E.
Bischoffen
Bischoffen is a small municipality in the central German state of Hesse, situated in a rural area characterized by forests, hills, and nearby reservoirs.
- 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_69ca848f55e48190b3f67252571c3d45 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd9c6aa01c819083fc758470096ec2 |
completed | April 1, 2026, 10:30 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d18a2d030c8190ada52e855bc9afc8 |
completed | April 4, 2026, 10:01 p.m. |
Created at: March 30, 2026, 8:15 p.m.