Triple
T1414367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anna Cornelia Carbentus |
E31878
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Etten |
E125661
|
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: Etten | Statement: [Anna Cornelia Carbentus, residence, Etten]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Etten Context triple: [Anna Cornelia Carbentus, residence, Etten]
-
A.
Etten
chosen
Etten is a village in the Netherlands known as one of the early places where Vincent van Gogh lived and worked.
-
B.
Emmen
Emmen is a major town and economic center in the northeastern Netherlands, known for its modern urban layout and attractions such as the Wildlands Adventure Zoo.
-
C.
Uden
Uden is a town in the southern Netherlands known for its location in the province of North Brabant and its proximity to nature reserves and regional industry.
-
D.
Vallentuna
Vallentuna is a locality in Stockholm County, Sweden, known as a suburban community within the Stockholm metropolitan area.
-
E.
Unna
Unna is a town in the German state of North Rhine-Westphalia, known historically as a regional trading center near Dortmund.
- 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_69a49919a994819086528951bc224775 |
completed | March 1, 2026, 7:52 p.m. |
| NER | Named-entity recognition | batch_69a4c3e5f9d08190861206934cd71fd8 |
completed | March 1, 2026, 10:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ace5812abc819091894509e9d6bd77 |
completed | March 8, 2026, 2:57 a.m. |
Created at: March 1, 2026, 7:59 p.m.