Triple
T29190327
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Varykino retreat |
E739971
|
entity |
| Predicate | filmingLocationRegion |
P138292
|
FINISHED |
| Object | Lake Como |
—
|
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: Lake Como | Statement: [Varykino retreat, filmingLocationRegion, Lake Como]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmingLocationRegion Context triple: [Varykino retreat, filmingLocationRegion, Lake Como]
-
A.
primaryFilmingRegions
chosen
Indicates the main geographic areas where the filming or production of a work primarily took place.
-
B.
countryOfFilming
Indicates the country where the filming or production of a work physically took place.
-
C.
filmingCountry
Indicates the country where the filming or primary production of a work took place.
-
D.
coProductionRegions
Indicates the regions or countries that jointly participated in producing a work (e.g., a film or TV show) as co-producers.
-
E.
filmingLocationContext
Indicates the contextual relationship specifying where the filming of an event, scene, or production took place.
- 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_69f07cb8033c8190b8807e219a14333d |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f6645ba71c81908044ade6ab577018 |
completed | May 2, 2026, 8:53 p.m. |
| PD | Predicate disambiguation | batch_69f663362c008190a22afed262f1e426 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 28, 2026, 12:02 p.m.