Triple
T31325565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eeva von Bock |
E798875
|
entity |
| Predicate | spouseOccupationInFiction |
P193197
|
FINISHED |
| Object | nobleman |
—
|
LITERAL 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: nobleman | Statement: [Eeva von Bock, spouseOccupationInFiction, nobleman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOccupationInFiction Context triple: [Eeva von Bock, spouseOccupationInFiction, nobleman]
-
A.
spouseOccupationInSeries
Indicates that a character’s spouse has a particular occupation within the context of a series.
-
B.
laterOccupationInFiction
Indicates that a fictional character holds a particular occupation at a later point in the narrative or timeline, distinct from their earlier roles.
-
C.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
D.
spouseCharacterOf
Indicates a marital relationship where one character is the spouse of another character.
-
E.
spouseCharacterPlayed
Indicates that one entity is the spouse of the character portrayed by another entity.
- F. None of above. chosen
Provenance (4 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_69f224e3238c8190b2291f50ea4962cd |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fd3a69f1e08190a11aed015bff0858 |
completed | May 8, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69fd39124180819080ca7911d3515d6d |
completed | May 8, 2026, 1:14 a.m. |
| PDg | Predicate description generation | batch_69fd3a6905b88190ae12b43576f4cc63 |
completed | May 8, 2026, 1:20 a.m. |
Created at: April 29, 2026, 9:15 p.m.