Triple
T24050683
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maureen Hayes Mansfield |
E595648
|
entity |
| Predicate | associatedWithInstitutionThroughSpouse |
P10641
|
FINISHED |
| Object | United States Senate |
—
|
NE NERFINISHED |
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: United States Senate | Statement: [Maureen Hayes Mansfield, associatedWithInstitutionThroughSpouse, United States Senate]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedWithInstitutionThroughSpouse Context triple: [Maureen Hayes Mansfield, associatedWithInstitutionThroughSpouse, United States Senate]
-
A.
associatedWithInstitution
Indicates that an entity has a formal or recognized connection or affiliation with an institution.
-
B.
associatedWithFieldThroughSpouse
Indicates that an entity is connected to a particular field or domain by virtue of their spouse’s involvement or association with that field.
-
C.
spouseInstanceOf
Indicates that one entity is the specific spouse (marriage partner) instance of another entity.
-
D.
associatedInstitution
Indicates that an entity has a formal connection or affiliation with a particular institution.
-
E.
spouseMemberOf
chosen
Indicates that a person’s spouse is a member of a specified group, organization, or entity.
- 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_69e288c184b081909f1f1751fb8e299a |
completed | April 17, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69f1d9d1527c8190a4cd30bb1988a263 |
completed | April 29, 2026, 10:13 a.m. |
| PD | Predicate disambiguation | batch_69f1764345388190a3102b62ddb729b4 |
completed | April 29, 2026, 3:08 a.m. |
Created at: April 17, 2026, 10:20 p.m.