Triple
T678168
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nijmegen |
E13123
|
entity |
| Predicate | borderedBy |
P224
|
FINISHED |
| Object |
Wijchen
Wijchen is a town and municipality in the Dutch province of Gelderland, located just southwest of the city of Nijmegen.
|
E303012
|
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: Wijchen | Statement: [Nijmegen, borderedBy, Wijchen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Wijchen Context triple: [Nijmegen, borderedBy, Wijchen]
-
A.
Gorinchem
Gorinchem is a historic fortified city in the Netherlands known for its well-preserved city walls and picturesque old town.
-
B.
Nijmegen
Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
-
C.
Culemborg
Culemborg is a historic town in the Dutch province of Gelderland, known for its medieval center and role in the early Dutch colonial era.
-
D.
Utrecht
Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
-
E.
Apeldoorn
Apeldoorn is a city in the province of Gelderland in the Netherlands, known for the royal palace Het Loo and its historical ties to the Dutch monarchy.
- 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: Wijchen Triple: [Nijmegen, borderedBy, Wijchen]
Generated description
Wijchen is a town and municipality in the Dutch province of Gelderland, located just southwest of the city of Nijmegen.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Wijchen Target entity description: Wijchen is a town and municipality in the Dutch province of Gelderland, located just southwest of the city of Nijmegen.
-
A.
Gorinchem
Gorinchem is a historic fortified city in the Netherlands known for its well-preserved city walls and picturesque old town.
-
B.
Nijmegen
Nijmegen is a historic Dutch city near the German border that played a crucial strategic role during World War II, particularly in the Allied advance in 1944.
-
C.
Culemborg
Culemborg is a historic town in the Dutch province of Gelderland, known for its medieval center and role in the early Dutch colonial era.
-
D.
Utrecht
Utrecht is a historic city and province in the central Netherlands, known for its medieval old town, canals, and role as a religious and cultural center.
-
E.
Apeldoorn
Apeldoorn is a city in the province of Gelderland in the Netherlands, known for the royal palace Het Loo and its historical ties to the Dutch monarchy.
- 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_69a4933d3bf88190972041cd8cf143b9 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a04e17088190943d54977eb3f83a |
completed | March 1, 2026, 8:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afe849ea708190be6393a21fd6da33 |
completed | March 10, 2026, 9:45 a.m. |
| NEDg | Description generation | batch_69afe948ba148190a88126df32f16be2 |
completed | March 10, 2026, 9:50 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0018170908190976f849380841b17 |
completed | March 10, 2026, 11:33 a.m. |
Created at: March 1, 2026, 7:36 p.m.