Triple
T10277145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skylene Montgomery |
E240996
|
entity |
| Predicate | sportAffiliationThroughSpouse |
P45903
|
FINISHED |
| Object | American football |
—
|
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: American football | Statement: [Skylene Montgomery, sportAffiliationThroughSpouse, American football]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: sportAffiliationThroughSpouse Context triple: [Skylene Montgomery, sportAffiliationThroughSpouse, American football]
-
A.
spouseSport
chosen
Indicates that the sport is the one played or practiced by a person's spouse.
-
B.
spousePlaysForLeague
Indicates that a person's spouse is an athlete who plays for a team in the specified league.
-
C.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
D.
hasSpouseTeam
Indicates that one entity is a team associated with, or belonging to, the spouse of another entity.
-
E.
spouseMemberOf
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_69d381a94c1881908fc38fc263d9b9c2 |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d7ccb7ec8190a538cf279e48116e |
completed | April 7, 2026, 10:09 a.m. |
| PD | Predicate disambiguation | batch_69d4d1f117708190928f92ae2611d724 |
completed | April 7, 2026, 9:44 a.m. |
Created at: April 6, 2026, 11:37 a.m.