Triple
T17373070
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mette Frederiksen |
E422364
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Mette |
E833688
|
NE FINISHED |
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: Mette | Statement: [Mette Frederiksen, givenName, Mette]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mette Context triple: [Mette Frederiksen, givenName, Mette]
-
A.
Mette
chosen
Mette is a given name most notably associated with American dancer and actress Mette Towley, known for her work in music videos and film.
-
B.
Jette
Jette is a municipality in the Brussels-Capital Region of Belgium, known for its residential character and educational institutions, including the Jette campus.
-
C.
Metter
The Metter is a river in Germany that flows through the state of Baden-Württemberg and ultimately joins the Enz River.
-
D.
Nammen
Nammen is a former municipality in North Rhine-Westphalia, Germany, that was incorporated into the town of Porta Westfalica.
-
E.
Grenaa
Grenaa is a coastal town in eastern Jutland, Denmark, known for its ferry connections to the island of Anholt and its role as a regional commercial and educational center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d889d6535c81908be333c01deaec4e |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e43a6a9ec881908bfe49413826d37e |
completed | April 19, 2026, 2:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a019fef65b48190811f2e034672b572 |
completed | May 11, 2026, 9:22 a.m. |
Created at: April 10, 2026, 5:44 a.m.