Triple
T17890432
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Flemming Rose |
E447300
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Flemming Rose |
—
|
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: Flemming Rose | Statement: [Flemming Rose, name, Flemming Rose]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Flemming Rose Context triple: [Flemming Rose, name, Flemming Rose]
-
A.
Flemming Rose
chosen
Flemming Rose is a Danish journalist and author best known for commissioning the controversial Muhammad cartoons published in Jyllands-Posten in 2005.
-
B.
Søren Sveistrup
Søren Sveistrup is a Danish screenwriter best known for creating the acclaimed crime series "The Killing" and for his work on various Nordic noir projects.
-
C.
Knut Vollebæk
Knut Vollebæk is a Norwegian diplomat and former foreign minister known for his work in international conflict prevention and minority rights.
-
D.
Anders Warming
Anders Warming is a Danish automobile designer best known for his work with BMW and MINI, where he led the design of several notable models.
-
E.
Godtfred Kirk Christiansen
Godtfred Kirk Christiansen was a Danish industrialist who expanded and globalized the LEGO toy company founded by his father, helping transform it into a world-leading brand.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9f59bd48190a6fc925a855b8bac |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49d7828b481909b645fceb37a7ca3 |
completed | April 19, 2026, 9:16 a.m. |
Created at: April 10, 2026, 10:18 a.m.