Triple
T4934504
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marie Witschi-Courant |
E110779
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Marie Witschi-Courant |
E110779
|
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: Marie Witschi-Courant | Statement: [Marie Witschi-Courant, name, Marie Witschi-Courant]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Marie Witschi-Courant Context triple: [Marie Witschi-Courant, name, Marie Witschi-Courant]
-
A.
Marie Witschi-Courant
chosen
Marie Witschi-Courant was the wife of Swiss surgeon and Nobel laureate Emil Theodor Kocher.
-
B.
Marie Elisabeth Saedler
Marie Elisabeth Saedler was the wife of Estonian writer and national epic compiler Friedrich Reinhold Kreutzwald.
-
C.
Louise Weiss
Louise Weiss was a prominent French author, journalist, feminist, and European politician known for her advocacy of women's rights and European integration.
-
D.
Marie-Thérèse Walter
Marie-Thérèse Walter was a French woman best known as Pablo Picasso’s muse and lover, who inspired many of his most celebrated portraits and sculptures in the 1930s.
-
E.
Albertina Rasch
Albertina Rasch was an influential early 20th-century choreographer and dance director known for her work on Broadway and in Hollywood films.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7066ed548190a76a9559f90e3869 |
completed | March 20, 2026, 4:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be77b74c748190a995a26f45b79ee9 |
completed | March 21, 2026, 10:49 a.m. |
Created at: March 20, 2026, 1:30 p.m.