Triple
T10562289
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Al Bano and Romina Power |
E249247
|
entity |
| Predicate | spouseMembers |
P13
|
FINISHED |
| Object | Al Bano and Romina Power were married to each other |
—
|
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: Al Bano and Romina Power were married to each other | Statement: [Al Bano and Romina Power, spouseMembers, Al Bano and Romina Power were married to each other]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseMembers Context triple: [Al Bano and Romina Power, spouseMembers, Al Bano and Romina Power were married to each other]
-
A.
spouseFamily
Indicates a family relationship formed through marriage, such as between a person and their spouse’s relatives.
-
B.
spouseMemberOf
Indicates that a person’s spouse is a member of a specified group, organization, or entity.
-
C.
spouseInFamily
Indicates that a person is a spouse (married partner) within the context of a specific family unit.
-
D.
spouse
chosen
Indicates that two entities are married to each other in a legally or socially recognized partnership.
-
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_69d381c8bd708190acf3d275c908251e |
completed | April 6, 2026, 9:50 a.m. |
| NER | Named-entity recognition | batch_69d527212dd081908629d91ce08f96a0 |
completed | April 7, 2026, 3:47 p.m. |
| PD | Predicate disambiguation | batch_69d51901ff6c819095e7b528170a69dc |
completed | April 7, 2026, 2:47 p.m. |
Created at: April 6, 2026, 12:36 p.m.