Triple
T12683759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wijchen |
E303012
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Beuningen
Beuningen is a municipality and town in the Dutch province of Gelderland, located near the city of Nijmegen in the eastern Netherlands.
|
E1003891
|
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: Beuningen | Statement: [Wijchen, borderedBy, Beuningen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Beuningen Context triple: [Wijchen, borderedBy, Beuningen]
-
A.
Beinsdorp
Beinsdorp is a small village in the Dutch province of North Holland, situated within the municipality of Haarlemmermeer.
-
B.
Veeningen
Veeningen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
-
C.
Betuwe
Betuwe is a fertile riverine region in the Dutch province of Gelderland, renowned for its extensive fruit orchards and scenic landscapes between the Rhine and Waal rivers.
-
D.
Zwanenburg
Zwanenburg is a village in North Holland, Netherlands, situated near Amsterdam and known as a suburban residential community within the Haarlemmermeer municipality.
-
E.
Veenendaal
Veenendaal is a Dutch town and municipality in the central Netherlands, known for its location between Utrecht and the Veluwe and its mix of residential, commercial, and light industrial areas.
- 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: Beuningen Triple: [Wijchen, borderedBy, Beuningen]
Generated description
Beuningen is a municipality and town in the Dutch province of Gelderland, located near the city of Nijmegen in the eastern Netherlands.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Beuningen Target entity description: Beuningen is a municipality and town in the Dutch province of Gelderland, located near the city of Nijmegen in the eastern Netherlands.
-
A.
Beinsdorp
Beinsdorp is a small village in the Dutch province of North Holland, situated within the municipality of Haarlemmermeer.
-
B.
Veeningen
Veeningen is a small village in the Dutch province of Drenthe, located within the municipality of De Wolden.
-
C.
Betuwe
Betuwe is a fertile riverine region in the Dutch province of Gelderland, renowned for its extensive fruit orchards and scenic landscapes between the Rhine and Waal rivers.
-
D.
Zwanenburg
Zwanenburg is a village in North Holland, Netherlands, situated near Amsterdam and known as a suburban residential community within the Haarlemmermeer municipality.
-
E.
Veenendaal
Veenendaal is a Dutch town and municipality in the central Netherlands, known for its location between Utrecht and the Veluwe and its mix of residential, commercial, and light industrial areas.
- 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_69d7bdee64a08190801c6d470aefd723 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d961d68358819095bdaab8adf1dcf0 |
completed | April 10, 2026, 8:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f68eafd4f8819083f20d142e9115ae |
completed | May 2, 2026, 11:54 p.m. |
| NEDg | Description generation | batch_69f68f8d2ca08190a385635fb6130a9f |
completed | May 2, 2026, 11:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f69033a66481908bf4ae23fced5983 |
completed | May 3, 2026, midnight |
Created at: April 9, 2026, 5:21 p.m.