Triple
T25407956
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actor for "Life with Father" |
E636607
|
entity |
| Predicate | nomineeCitizenship |
P2
|
FINISHED |
| Object | United States |
—
|
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 | Statement: [Academy Award for Best Actor for "Life with Father", nomineeCitizenship, United States]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nomineeCitizenship Context triple: [Academy Award for Best Actor for "Life with Father", nomineeCitizenship, United States]
-
A.
countryOfCitizenship
chosen
Indicates the country in which a person or entity holds legal citizenship.
-
B.
hasLaureateCitizenship
Indicates that a laureate holds or has held citizenship in a specified country or political entity.
-
C.
officeHolderNationality
Indicates that the nationality of an office holder is a specified country or nation.
-
D.
nationRepresented
Indicates that one entity serves as the official national affiliation or country represented by another entity, such as an individual, team, or organization.
-
E.
creatorCountryOfCitizenship
Indicates the country in which the creator holds or held legal citizenship.
- 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_69e75db361d881908d8701c856da6413 |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5ffc74fa481909b4fe24a9337f9eb |
completed | May 2, 2026, 1:44 p.m. |
| PD | Predicate disambiguation | batch_69f5f7f99dc08190afcfb3bc4dfbec1d |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 21, 2026, 1:52 p.m.