Triple
T5566336
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Best Animated Short Subject |
E145888
|
entity |
| Predicate | languageOfAwardName |
P17628
|
FINISHED |
| Object | English |
—
|
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: English | Statement: [Best Animated Short Subject, languageOfAwardName, English]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfAwardName Context triple: [Best Animated Short Subject, languageOfAwardName, English]
-
A.
awardNameLanguage
chosen
Indicates the language in which the name of an award is expressed.
-
B.
languageOfAwardingInstitution
Indicates the language in which the awarding institution formally grants or documents the award.
-
C.
awardLanguage
Indicates that an award is given specifically for works or achievements in a particular language.
-
D.
officialLanguageOfNomination
Indicates the language officially used in the nomination process or documentation for a given entity.
-
E.
languageOfMostNominations
Indicates the language in which the highest number of nominations (e.g., for awards or recognitions) have been made within a given context.
- 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_69c008fdae24819081aa002ad99cd966 |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c02034fc3081908920c52a19d462e1 |
completed | March 22, 2026, 5 p.m. |
| PD | Predicate disambiguation | batch_69c01b12826c8190969a584d0f53aa44 |
completed | March 22, 2026, 4:38 p.m. |
Created at: March 22, 2026, 3:36 p.m.