Triple
T33442738
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | European Film Award for Best Cinematographer |
E856406
|
entity |
| Predicate | isNonFictionEligible |
P178654
|
FINISHED |
| Object | yes, depending on year and rules |
—
|
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: yes, depending on year and rules | Statement: [European Film Award for Best Cinematographer, isNonFictionEligible, yes, depending on year and rules]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isNonFictionEligible Context triple: [European Film Award for Best Cinematographer, isNonFictionEligible, yes, depending on year and rules]
-
A.
isNonFictionCategory
Indicates that a given category pertains to non-fiction works, such as factual or informational content rather than fictional material.
-
B.
isNonfiction
Indicates that the work or content is factual rather than fictional, based on real events, people, or information.
-
C.
hasFictionComponent
Indicates that something includes, contains, or is composed in part of a fictional element or work.
-
D.
nonFictionAbout
Indicates that a non-fiction work has content focused on, discusses, or is about a particular subject or entity.
-
E.
hasWrittenNonFiction
Indicates that a person is the author of one or more non-fiction works.
- 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_69f34971b75881908be360bb041f003c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f713bfdc148190a249a7874320bab8 |
completed | May 3, 2026, 9:22 a.m. |
| PD | Predicate disambiguation | batch_69f7127884388190884f23d181a65d19 |
completed | May 3, 2026, 9:16 a.m. |
| PDg | Predicate description generation | batch_69f7135fa2988190a20a94cfe616d754 |
completed | May 3, 2026, 9:20 a.m. |
Created at: May 1, 2026, 1:37 a.m.