Triple
T1952246
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MTV Movie & TV Award for Best Performance in a Movie |
E42182
|
entity |
| Predicate | awardMedium |
P33478
|
FINISHED |
| Object | motion pictures |
—
|
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: motion pictures | Statement: [MTV Movie & TV Award for Best Performance in a Movie, awardMedium, motion pictures]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: awardMedium Context triple: [MTV Movie & TV Award for Best Performance in a Movie, awardMedium, motion pictures]
-
A.
awardType
Indicates the specific category or kind of award associated with an entity or event.
-
B.
awardGivenBy
Indicates that an award is conferred or presented by one entity to another.
-
C.
awardConferred
Indicates that an award or honor has been formally granted by one entity to another.
-
D.
awardName
Indicates the specific name or title of an award associated with an entity.
-
E.
awardFor
Indicates that something is given or granted as recognition or a prize for a particular achievement, work, or contribution.
- 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_69a8870e08fc8190a319cbf2600db15f |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb34eb5748190a3ac395252951eba |
completed | March 7, 2026, 5:10 a.m. |
| PD | Predicate disambiguation | batch_69abaff3eda88190b643994cb4dfb8df |
completed | March 7, 2026, 4:56 a.m. |
| PDg | Predicate description generation | batch_69abb1ddccbc8190bf2bd8bac673c0c5 |
completed | March 7, 2026, 5:04 a.m. |
Created at: March 4, 2026, 7:36 p.m.