Triple
T25407955
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actor for "Life with Father" |
E636607
|
entity |
| Predicate | nomineeOccupation |
P10684
|
FINISHED |
| Object | actor |
—
|
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: actor | Statement: [Academy Award for Best Actor for "Life with Father", nomineeOccupation, actor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: nomineeOccupation Context triple: [Academy Award for Best Actor for "Life with Father", nomineeOccupation, actor]
-
A.
notableAwardNominationRecipient
chosen
Indicates that an entity has been formally nominated to receive a particular notable award.
-
B.
sonOccupation
Indicates that a specified occupation is the job or professional role held by a person's son.
-
C.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
D.
hasNomineeRole
Indicates that an entity serves in or holds a specific nominee role or position in relation to another entity.
-
E.
laureateOccupation
Indicates the professional role or field in which a laureate is recognized or has worked.
- 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_69f5f6baf2d48190a6a4cd6501be87d2 |
completed | May 2, 2026, 1:06 p.m. |
| PD | Predicate disambiguation | batch_69f5afd5baac8190bb8ed576813c8591 |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 1:52 p.m.