Triple
T345858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Snake Pit |
E6939
|
entity |
| Predicate | academyAwardForBestActress |
P12610
|
FINISHED |
| Object | nominated |
—
|
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: nominated | Statement: [The Snake Pit, academyAwardForBestActress, nominated]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: academyAwardForBestActress Context triple: [The Snake Pit, academyAwardForBestActress, nominated]
-
A.
bestActressWinner
Indicates that the subject has won the Best Actress award in a given competition or context.
-
B.
bestSupportingActressWinner
Indicates that the subject is the person who won the Best Supporting Actress award for the specified work, year, or event.
-
C.
bestActorWinner
Indicates that the subject is the recipient of a "Best Actor" award for a particular performance or event.
-
D.
bestPictureWinner
Indicates that the subject is the film that won the Best Picture award in a given context or year.
-
E.
bestSupportingActressFilm
Indicates the film for which a person received or was nominated for a Best Supporting Actress award.
- F. None of above. chosen
Provenance (4 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_69a2e7951ba08190960e90823b5078f3 |
completed | Feb. 28, 2026, 1:03 p.m. |
| NER | Named-entity recognition | batch_69a2eb0240e88190bc70784772f5fa30 |
completed | Feb. 28, 2026, 1:17 p.m. |
| PD | Predicate disambiguation | batch_69a2e95451a4819090f4e4fb9b21a493 |
completed | Feb. 28, 2026, 1:10 p.m. |
| PDg | Predicate description generation | batch_69a2eae0bd7081908197bbf5c55fe647 |
completed | Feb. 28, 2026, 1:17 p.m. |
Created at: Feb. 28, 2026, 1:08 p.m.