Triple
T14060384
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Verhagen |
E338328
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object |
Verhaegen
Verhaegen is a Dutch-language surname of Belgian and Dutch origin, borne by various notable figures in politics, academia, and the arts.
|
E1080055
|
NE FINISHED |
How this triple was built (4 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: Verhaegen | Statement: [Verhagen, hasVariant, Verhaegen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verhaegen Context triple: [Verhagen, hasVariant, Verhaegen]
-
A.
Kortenaer
Kortenaer is a Dutch surname most notably associated with the 17th-century admiral Egbert Bartholomeusz Kortenaer.
-
B.
Verhoeven
Verhoeven is a Dutch surname most prominently associated with filmmaker Paul Verhoeven, known for his provocative and influential works in both European and Hollywood cinema.
-
C.
Van der Madeweg
Van der Madeweg is a metro station in Amsterdam that serves as a stop on the city's rapid transit network.
-
D.
van Heutsz
Van Heutsz is a Dutch surname most prominently associated with Johannes Benedictus van Heutsz, a colonial military leader and Governor-General of the Dutch East Indies.
-
E.
De Haas
De Haas is a surname of Dutch origin, often associated with individuals and families in the Netherlands and abroad.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Verhaegen Triple: [Verhagen, hasVariant, Verhaegen]
Generated description
Verhaegen is a Dutch-language surname of Belgian and Dutch origin, borne by various notable figures in politics, academia, and the arts.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Verhaegen Target entity description: Verhaegen is a Dutch-language surname of Belgian and Dutch origin, borne by various notable figures in politics, academia, and the arts.
-
A.
Kortenaer
Kortenaer is a Dutch surname most notably associated with the 17th-century admiral Egbert Bartholomeusz Kortenaer.
-
B.
Verhoeven
Verhoeven is a Dutch surname most prominently associated with filmmaker Paul Verhoeven, known for his provocative and influential works in both European and Hollywood cinema.
-
C.
Van der Madeweg
Van der Madeweg is a metro station in Amsterdam that serves as a stop on the city's rapid transit network.
-
D.
van Heutsz
Van Heutsz is a Dutch surname most prominently associated with Johannes Benedictus van Heutsz, a colonial military leader and Governor-General of the Dutch East Indies.
-
E.
De Haas
De Haas is a surname of Dutch origin, often associated with individuals and families in the Netherlands and abroad.
- F. None of above. chosen
Provenance (5 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_69d81c67ba6c819091935650dfb3b895 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5686f51c81908c33143ecbaae83d |
completed | April 14, 2026, 3 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fcd09db51c8190ab275e68b1aec120 |
completed | May 7, 2026, 5:49 p.m. |
| NEDg | Description generation | batch_69fcd340e25881908c2ff61a40da2e7a |
completed | May 7, 2026, 6 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fcd3fe5f5081909291e8b9af77ee33 |
completed | May 7, 2026, 6:03 p.m. |
Created at: April 9, 2026, 10:21 p.m.