Triple
T18642603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Miroslava Federer |
E455726
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Miroslava Federer |
—
|
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: Miroslava Federer | Statement: [Miroslava Federer, name, Miroslava Federer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Miroslava Federer Context triple: [Miroslava Federer, name, Miroslava Federer]
-
A.
Miroslava Federer
chosen
Miroslava Federer is a former Slovak-born Swiss professional tennis player and the wife of tennis legend Roger Federer.
-
B.
Elia Zurbriggen
Elia Zurbriggen is a Swiss alpine skier known for competing in international skiing competitions.
-
C.
Heidi Zurbriggen
Heidi Zurbriggen is a former Swiss alpine skier who competed at the international level in the late 20th century.
-
D.
Sanja Fidler
Sanja Fidler is a computer vision researcher and professor known for her work on 3D scene understanding, deep learning, and their applications in graphics and autonomous systems.
-
E.
Lara Gut-Behrami
Lara Gut-Behrami is a Swiss World Cup alpine ski racer and Olympic champion known for her success in speed events such as super-G and downhill.
- 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_69d8d38ea1e88190997e9b231190ba6f |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54fcd6da081908030b052727f2c2f |
completed | April 19, 2026, 9:57 p.m. |
Created at: April 10, 2026, 11:47 a.m.