Triple
T6075486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Everdine Huberta van Wijnbergen |
E135389
|
entity |
| Predicate | spouse occupation |
P4765
|
FINISHED |
| Object | writer |
—
|
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: writer | Statement: [Everdine Huberta van Wijnbergen, spouse occupation, writer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouse occupation Context triple: [Everdine Huberta van Wijnbergen, spouse occupation, writer]
-
A.
spouseOccupation
chosen
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouse
Indicates that two entities are married to each other in a legally or socially recognized partnership.
-
C.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
D.
spouseOffice
Indicates that one entity holds an office or position that is associated with, or held by, the spouse of another entity.
-
E.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
- 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_69c0087ad31c8190ab936e0ff28614b6 |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c0575ec63081908a868a41855acf73 |
completed | March 22, 2026, 8:55 p.m. |
| PD | Predicate disambiguation | batch_69c049f21fe08190995df3c5c05fb8ea |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:11 p.m.