Triple
T32555977
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Shape of Water |
E832096
|
entity |
| Predicate | AcademyAwardForBestPictureYear |
P3532
|
FINISHED |
| Object | 2017 film year |
—
|
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: 2017 film year | Statement: [The Shape of Water, AcademyAwardForBestPictureYear, 2017 film year]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: AcademyAwardForBestPictureYear Context triple: [The Shape of Water, AcademyAwardForBestPictureYear, 2017 film year]
-
A.
oscarBestPictureYear
chosen
Indicates the year in which a given film received the Academy Award for Best Picture.
-
B.
AcademyAwardsYear
Indicates the specific year in which the referenced Academy Awards event took place.
-
C.
oscarAward
Indicates that an entity has received or been honored with an Academy Award (Oscar).
-
D.
bestPictureWinner
Indicates that the subject is the film that won the Best Picture award in a given context or year.
-
E.
academyAwardsBestPictureCount
Indicates the number of Academy Awards won for Best Picture associated with an entity.
- 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_69f34926b9848190ace47d2dd0a0de7c |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c5fbbd308190b7af8be4c25e6ff1 |
completed | May 3, 2026, 3:50 a.m. |
| PD | Predicate disambiguation | batch_69f6bd2a14b081908162923dfbf0a6f4 |
completed | May 3, 2026, 3:12 a.m. |
Created at: May 1, 2026, 1:03 a.m.