Triple
T12067764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josef Strauss |
E287340
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Josef |
E66212
|
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: Josef | Statement: [Josef Strauss, givenName, Josef]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Josef Context triple: [Josef Strauss, givenName, Josef]
-
A.
Jozef
chosen
Jozef is a masculine given name of Hebrew origin, commonly used in Central and Eastern Europe as a variant of Joseph.
-
B.
Josef David
Josef David was the husband of Ottla Kafka, the youngest sister of writer Franz Kafka, and a member of the Prague Jewish community in the early 20th century.
-
C.
Josef Jennewein
Josef Jennewein was a German World War II Luftwaffe fighter ace and former Olympic alpine skier.
-
D.
Franz
Franz is the given name of Franz Cardinal König, a prominent 20th-century Austrian Catholic cardinal and influential church leader.
-
E.
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.
- 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_69d6ab4846e081908ee7bbd66a6d3459 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d904423dc08190a47194422255c62e |
completed | April 10, 2026, 2:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64b8205a481909cf8cf6fb35b9509 |
completed | May 2, 2026, 7:07 p.m. |
Created at: April 8, 2026, 9:48 p.m.