Triple
T25661883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mein Leben mit Ernst Cassirer |
E643404
|
entity |
| Predicate | hasSpouseOfMainSubject |
P33561
|
FINISHED |
| Object | Toni Cassirer |
—
|
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: Toni Cassirer | Statement: [Mein Leben mit Ernst Cassirer, hasSpouseOfMainSubject, Toni Cassirer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpouseOfMainSubject Context triple: [Mein Leben mit Ernst Cassirer, hasSpouseOfMainSubject, Toni Cassirer]
-
A.
spouseAssociatedWith
chosen
Indicates a marital or spousal relationship or close association between two entities.
-
B.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
C.
spouseOfHead
Indicates that one person is the married partner of the individual who holds the position of head (e.g., head of a household, organization, or state).
-
D.
spouseCharacterOf
Indicates a marital relationship where one character is the spouse of another character.
-
E.
hasAuthorSpouse
Indicates that the spouse of the subject entity is the author of the related work or entity.
- F. None of above.
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_69e77e7e45648190a068ed3faa8016ea |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 21, 2026, 6:55 p.m.