Triple
T25158722
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 19th Academy Awards |
E626382
|
entity |
| Predicate | bestForeignLanguageFilmSpecialAward |
P68129
|
FINISHED |
| Object | Shoe-Shine |
—
|
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: Shoe-Shine | Statement: [19th Academy Awards, bestForeignLanguageFilmSpecialAward, Shoe-Shine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bestForeignLanguageFilmSpecialAward Context triple: [19th Academy Awards, bestForeignLanguageFilmSpecialAward, Shoe-Shine]
-
A.
bestForeignLanguageFilmWinner
Indicates that the subject is the film that won the award for Best Foreign Language Film in the specified context or event.
-
B.
bestForeignFilmHonoraryAwardCountry
Indicates the country that received an honorary award for best foreign film.
-
C.
bestForeignFilmHonoraryAwardRecipient
chosen
Indicates that an entity received an honorary award recognizing it as the best foreign film.
-
D.
academyAwardBestForeignLanguageFilmNomination
Indicates that a film received a nomination for the Academy Award for Best Foreign Language Film.
-
E.
academyAwardBestForeignLanguageFilmWinCount
Indicates the number of times an entity has won the Academy Award for Best Foreign Language Film.
- 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_69e2ff2834ec8190b0872e2ec3d76023 |
completed | April 18, 2026, 3:48 a.m. |
| NER | Named-entity recognition | batch_69f46b8b03508190ad7ef11ca65eda05 |
completed | May 1, 2026, 8:59 a.m. |
| PD | Predicate disambiguation | batch_69f44d8043b081908bbffd7f044b4f26 |
completed | May 1, 2026, 6:51 a.m. |
Created at: April 18, 2026, 6:31 a.m.