Triple
T1931999
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans van Heeswijk |
E40965
|
entity |
| Predicate | workLocation |
P7
|
FINISHED |
| Object |
Gorssel
Gorssel is a village in the Dutch province of Gelderland, known for its scenic rural character and as the location of Museum MORE for modern realism.
|
E315614
|
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: Gorssel | Statement: [Hans van Heeswijk, workLocation, Gorssel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gorssel Context triple: [Hans van Heeswijk, workLocation, Gorssel]
-
A.
Rudolfswerth
Rudolfswerth is the former German name for Novo Mesto, a historic town in southeastern Slovenia known for its medieval heritage and role as a regional cultural center.
-
B.
Göhren
Göhren is a seaside resort town on the Baltic Sea coast of Germany, located on the island of Rügen and known for its beaches and tourism.
-
C.
Coesfeld
Coesfeld is a town in the Münster region of North Rhine-Westphalia, Germany, known as a local administrative and commercial center.
-
D.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
E.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
- 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: Gorssel Triple: [Hans van Heeswijk, workLocation, Gorssel]
Generated description
Gorssel is a village in the Dutch province of Gelderland, known for its scenic rural character and as the location of Museum MORE for modern realism.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gorssel Target entity description: Gorssel is a village in the Dutch province of Gelderland, known for its scenic rural character and as the location of Museum MORE for modern realism.
-
A.
Rudolfswerth
Rudolfswerth is the former German name for Novo Mesto, a historic town in southeastern Slovenia known for its medieval heritage and role as a regional cultural center.
-
B.
Göhren
Göhren is a seaside resort town on the Baltic Sea coast of Germany, located on the island of Rügen and known for its beaches and tourism.
-
C.
Coesfeld
Coesfeld is a town in the Münster region of North Rhine-Westphalia, Germany, known as a local administrative and commercial center.
-
D.
Hasselwerder
Hasselwerder is a small island located in Lake Tegel in Berlin, Germany.
-
E.
Radevormwald
Radevormwald is a small historic town in North Rhine-Westphalia, western Germany, known for its hilly Bergisches Land landscape and traditional textile and metalworking industries.
- 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_69a8864711648190b07bed24ed76258e |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb297ec2c819092ad62d72005223d |
completed | March 7, 2026, 5:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b108b7617c8190938c7ed35e0a791e |
completed | March 11, 2026, 6:16 a.m. |
| NEDg | Description generation | batch_69b109aca8008190aa34902fb63fb1a3 |
completed | March 11, 2026, 6:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b10a5da7d08190967750728135ab68 |
completed | March 11, 2026, 6:23 a.m. |
Created at: March 4, 2026, 7:35 p.m.