Triple
T7904552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Gump – Sally Field |
E183538
|
entity |
| Predicate | filmAcademyAwards |
P10689
|
FINISHED |
| Object | Best Picture |
—
|
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: Best Picture | Statement: [Mrs. Gump – Sally Field, filmAcademyAwards, Best Picture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmAcademyAwards Context triple: [Mrs. Gump – Sally Field, filmAcademyAwards, Best Picture]
-
A.
academyAwardsEdition
Indicates the specific edition or installment of the Academy Awards associated with an entity.
-
B.
oscarAward
chosen
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
C.
filmEditingAcademyAward
Indicates that an entity received or is associated with an Academy Award specifically for film editing.
-
D.
mostAwardsFilm
Indicates that a film is the one that has received the highest number of awards within a given set or context.
-
E.
hasAcademy
Indicates that an entity possesses, operates, or is formally associated with an academy as part of its structure or offerings.
- 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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a4331cc8190b50301c78767a850 |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.