Triple
T24868199
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Antonio Rodolfo Quinn Oaxaca |
E622344
|
entity |
| Predicate | academyAwardForBestSupportingActorFor |
P8118
|
FINISHED |
| Object | Lust for Life |
—
|
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: Lust for Life | Statement: [Antonio Rodolfo Quinn Oaxaca, academyAwardForBestSupportingActorFor, Lust for Life]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academyAwardForBestSupportingActorFor Context triple: [Antonio Rodolfo Quinn Oaxaca, academyAwardForBestSupportingActorFor, Lust for Life]
-
A.
academyAwardForBestSupportingActorYear
Indicates the specific year in which a given Academy Award for Best Supporting Actor was awarded.
-
B.
bestSupportingActorWinner
Indicates that an entity has received the award for Best Supporting Actor for a particular work or event.
-
C.
bestSupportingActorWinningFilm
Indicates that a film is the one for which an actor won the Best Supporting Actor award.
-
D.
bestSupportingActorFilm
chosen
Indicates the film for which a person received the Best Supporting Actor award.
-
E.
bestSupportingActressWinner
Indicates that the subject is the person who won the Best Supporting Actress award for the specified work, year, or event.
- 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_69e2fac3fdbc81909c2ec49be5743cd9 |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f464b4c9b0819085daa00c7c3b8b76 |
completed | May 1, 2026, 8:30 a.m. |
| PD | Predicate disambiguation | batch_69f45cf017a88190b4985b11159c907d |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 18, 2026, 5:23 a.m.