Triple
T18731281
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award nomination |
E458039
|
entity |
| Predicate | hasNotableEffect |
P17691
|
FINISHED |
| Object | can increase box office revenue |
—
|
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: can increase box office revenue | Statement: [Academy Award nomination, hasNotableEffect, can increase box office revenue]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableEffect Context triple: [Academy Award nomination, hasNotableEffect, can increase box office revenue]
-
A.
notableEffect
Indicates that one entity has a significant impact, consequence, or influence on another entity or situation.
-
B.
hasEffectIn
Indicates that one entity produces, causes, or exerts an effect within a specified context, system, or environment.
-
C.
hasDirectEffect
Indicates that one entity produces an immediate and unmediated impact or change on another entity.
-
D.
hasNotableImpact
chosen
Indicates that one entity exerts a significant or noteworthy influence or effect on another entity or context.
-
E.
hasPharmacologicalEffect
Indicates that one entity produces a specific pharmacological effect or action on another 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_69d8d393ba9c8190a8b03b04ddbb0a09 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e56d778cf8819083500600b9ac0744 |
completed | April 20, 2026, 12:04 a.m. |
| PD | Predicate disambiguation | batch_69e48d03766c8190a43f7681842f4f8d |
completed | April 19, 2026, 8:06 a.m. |
Created at: April 10, 2026, 11:51 a.m.