Triple
T31405632
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Academy Award for Best Actor for Pelle the Conqueror |
E801117
|
entity |
| Predicate | filmCountryOfSetting |
P10686
|
FINISHED |
| Object | Denmark |
—
|
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: Denmark | Statement: [Academy Award for Best Actor for Pelle the Conqueror, filmCountryOfSetting, Denmark]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: filmCountryOfSetting Context triple: [Academy Award for Best Actor for Pelle the Conqueror, filmCountryOfSetting, Denmark]
-
A.
filmCountryOfOrigin
Indicates the country where a film was originally produced or created.
-
B.
countryOfFilming
Indicates the country where the filming or production of a work physically took place.
-
C.
countryOfSetting
chosen
Indicates the country in which the setting or context of something (such as a story, event, or work) takes place.
-
D.
filmingCountry
Indicates the country where the filming or primary production of a work took place.
-
E.
countryOfFictionalContext
Indicates that a work of fiction is primarily set in, or contextually associated with, a particular country.
- 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_69f348c0dd648190bf2fd7642f78eb06 |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_69f6a5f71b2c8190aade8a83f465be0c |
completed | May 3, 2026, 1:33 a.m. |
| PD | Predicate disambiguation | batch_69f69fe66df08190958558d63ee623d9 |
completed | May 3, 2026, 1:07 a.m. |
Created at: April 30, 2026, 8:31 p.m.