Triple
T12212233
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Luise Maas |
E290992
|
entity |
| Predicate | roleInSpouseCareer |
P103820
|
FINISHED |
| Object | personal and professional support |
—
|
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: personal and professional support | Statement: [Luise Maas, roleInSpouseCareer, personal and professional support]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: roleInSpouseCareer Context triple: [Luise Maas, roleInSpouseCareer, personal and professional support]
-
A.
spouseOccupation
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouseIndustry
Indicates the industry or sector in which a person's spouse is employed or primarily involved.
-
C.
spousePlaceOfWork
Indicates that the place of work specified belongs to the spouse of the referenced person.
-
D.
spouseInWork
Indicates that two entities are spouses within the context of a particular work (such as a book, film, or series), rather than in real life.
-
E.
spouseInFamily
Indicates that a person is a spouse (married partner) within the context of a specific family unit.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d920e312708190b4aede2e21f5f697 |
completed | April 10, 2026, 4:10 p.m. |
| PD | Predicate disambiguation | batch_69d91c3d669c81908eea7ad61122d275 |
completed | April 10, 2026, 3:50 p.m. |
| PDg | Predicate description generation | batch_69d920c3dc9881908c396a4ab34f4836 |
completed | April 10, 2026, 4:09 p.m. |
Created at: April 8, 2026, 9:51 p.m.