Triple
T26963530
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | House of Bernadotte |
E679109
|
entity |
| Predicate | originCountryOfFounder |
P30107
|
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: [House of Bernadotte, originCountryOfFounder, France]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: originCountryOfFounder Context triple: [House of Bernadotte, originCountryOfFounder, France]
-
A.
homeCountryDuringFounding
Indicates the country in which an entity (such as an organization or company) was based at the time it was founded.
-
B.
foundingLocationPresentCountry
Indicates the present-day country in which the location where something (typically an organization or institution) was founded is situated.
-
C.
historicalOriginCountry
Indicates the country from which something originally came or first emerged in a historical context.
-
D.
founderNationality
chosen
Indicates that the specified person’s country of origin or citizenship is the one associated with the founder in question.
-
E.
countryOfEponym
Indicates that the related entity is named after something (an eponym) originating from or 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_69eeeb4f3a448190b1e94b2d4776c16e |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69fd5f29b1988190877764ef2a399c7f |
completed | May 8, 2026, 3:57 a.m. |
| PD | Predicate disambiguation | batch_69fd5e30194c819085b5ce586122ab37 |
completed | May 8, 2026, 3:53 a.m. |
Created at: April 27, 2026, 6:33 a.m.