Triple
T34100198
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheryl Ruettiger |
E874545
|
entity |
| Predicate | spouseOfPersonDescribedAs |
P26253
|
FINISHED |
| Object | former Notre Dame football player |
—
|
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: former Notre Dame football player | Statement: [Cheryl Ruettiger, spouseOfPersonDescribedAs, former Notre Dame football player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOfPersonDescribedAs Context triple: [Cheryl Ruettiger, spouseOfPersonDescribedAs, former Notre Dame football player]
-
A.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
B.
hasSpouseDescribed
chosen
Indicates that one entity is described as the spouse of another entity.
-
C.
spouseOfType
Indicates that one entity is the spouse of another, specifying the type or role of that spousal relationship.
-
D.
describedBySpouseAs
Indicates that one person is characterized, portrayed, or referred to in a particular way by their spouse.
-
E.
spouseCharacterOf
Indicates a marital relationship where one character is the spouse of another character.
- 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_69f349a735208190a1dbfb1c2a121059 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69fddf721c1481909301a0f379368f10 |
completed | May 8, 2026, 1:04 p.m. |
| PD | Predicate disambiguation | batch_69fddda1ae7c8190b5848ff9a9e39826 |
completed | May 8, 2026, 12:57 p.m. |
Created at: May 1, 2026, 1:53 a.m.