Triple
T12137088
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Franciszek |
E289085
|
entity |
| Predicate | isEquivalentName |
P3889
|
FINISHED |
| Object | Franz |
E912185
|
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: Franz | Statement: [Franciszek, isEquivalentName, Franz]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Franz Context triple: [Franciszek, isEquivalentName, Franz]
-
A.
Franz
Franz is the given name of Frank X. Leyendecker, an American illustrator known for his magazine covers and advertising art in the early 20th century.
-
B.
Franz
Franz is the given name of Franz Cardinal König, a prominent 20th-century Austrian Catholic cardinal and influential church leader.
-
C.
Franz
Franz is a character in Louisa May Alcott's novel "Little Men," one of the boys at Plumfield School whose experiences reflect the book's themes of growth, education, and moral development.
-
D.
Franz
Franz is a German-language surname of Central European origin borne by various notable individuals.
-
E.
Franz
chosen
Franz is a masculine given name of German origin that has been borne by numerous notable figures in arts, science, and politics.
- 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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9158dd00c819082651891898b91bb |
completed | April 10, 2026, 3:21 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f65556e718819092736cd89c326fb5 |
completed | May 2, 2026, 7:49 p.m. |
Created at: April 8, 2026, 9:49 p.m.