Triple
T18013472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elaine Mendoza Erfe |
E430940
|
entity |
| Predicate | hasPublicFigureSpouse |
P130116
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Elaine Mendoza Erfe, hasPublicFigureSpouse, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasPublicFigureSpouse Context triple: [Elaine Mendoza Erfe, hasPublicFigureSpouse, true]
-
A.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
B.
spouseOfCountry
Indicates that an entity is the spouse or marital partner of a person who is associated with, represents, or is from a specified country.
-
C.
sometimesSpouseOf
Indicates that two entities are occasionally, but not consistently or permanently, in a spousal relationship with each other.
-
D.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
E.
hasNamesakeSpouse
Indicates that one entity has a spouse who shares the same name as another specified 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b521befc81908dff44f19aa3d580 |
completed | April 19, 2026, 10:57 a.m. |
| PD | Predicate disambiguation | batch_69e3f90039e4819080527f860dca042e |
completed | April 18, 2026, 9:34 p.m. |
| PDg | Predicate description generation | batch_69e42d8d68288190a05dc5d7803cf823 |
completed | April 19, 2026, 1:19 a.m. |
Created at: April 10, 2026, 10:24 a.m.