Triple
T25321140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | One Deadly Summer |
E634883
|
entity |
| Predicate | filmRatingSystemCountry |
P149658
|
FINISHED |
| Object | France |
—
|
NE NERFINISHED |
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: France | Statement: [One Deadly Summer, filmRatingSystemCountry, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmRatingSystemCountry Context triple: [One Deadly Summer, filmRatingSystemCountry, France]
-
A.
filmRatingCanada
Indicates the film’s official content rating as assigned by Canadian film classification authorities.
-
B.
filmRatingRegion
chosen
Indicates the region or country for which a film’s content rating or classification is applicable.
-
C.
filmRatingKorea
Indicates that a film has a specific official content rating assigned by the Korean rating authority.
-
D.
ageRatingSystem
Indicates the classification scheme or standard used to assign age-appropriateness ratings to content.
-
E.
filmCertificationIndia
Indicates the official age-appropriateness or content rating assigned to a film by the Indian certification authority.
- 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_69e75a9847c08190bb02990d06d5ffb7 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f4968e4e70819096256546d76f6e6b |
completed | May 1, 2026, 12:03 p.m. |
| PD | Predicate disambiguation | batch_69f45d06d0388190b36ecde92013624a |
completed | May 1, 2026, 7:57 a.m. |
Created at: April 21, 2026, 1:28 p.m.