Triple
T8527689
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wilrijk |
E201858
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object |
Berchem district
Berchem district is a suburban district of Antwerp, Belgium, known for its residential neighborhoods, Art Nouveau architecture, and important railway junction.
|
E743085
|
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: Berchem district | Statement: [Wilrijk, near, Berchem district]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berchem district Context triple: [Wilrijk, near, Berchem district]
-
A.
Diepenbeek
Diepenbeek is a municipality in the Belgian province of Limburg, known for its blend of residential areas, industry, and the campus of Hasselt University.
-
B.
Bellebeek
Bellebeek is a small stream in Belgium that serves as a right-bank tributary of the River Dender.
-
C.
Zonhoven
Zonhoven is a municipality in the Belgian province of Limburg, known for its green surroundings and proximity to the city of Hasselt.
-
D.
Bezuidenhout district
Bezuidenhout district is a central neighborhood in The Hague, Netherlands, known for its mix of offices, residential areas, and major transport connections.
-
E.
Lembeek
Lembeek is a village in the Belgian municipality of Halle, located along the Senne River in the province of Flemish Brabant.
- 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: Berchem district Triple: [Wilrijk, near, Berchem district]
Generated description
Berchem district is a suburban district of Antwerp, Belgium, known for its residential neighborhoods, Art Nouveau architecture, and important railway junction.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Berchem district Target entity description: Berchem district is a suburban district of Antwerp, Belgium, known for its residential neighborhoods, Art Nouveau architecture, and important railway junction.
-
A.
Diepenbeek
Diepenbeek is a municipality in the Belgian province of Limburg, known for its blend of residential areas, industry, and the campus of Hasselt University.
-
B.
Bellebeek
Bellebeek is a small stream in Belgium that serves as a right-bank tributary of the River Dender.
-
C.
Zonhoven
Zonhoven is a municipality in the Belgian province of Limburg, known for its green surroundings and proximity to the city of Hasselt.
-
D.
Bezuidenhout district
Bezuidenhout district is a central neighborhood in The Hague, Netherlands, known for its mix of offices, residential areas, and major transport connections.
-
E.
Lembeek
Lembeek is a village in the Belgian municipality of Halle, located along the Senne River in the province of Flemish Brabant.
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6477100819081fa20cb6b8ea3d7 |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce88f750cc819093c8902b2c285153 |
completed | April 2, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_69ce8b8a9cb08190b1860d8174a4400f |
completed | April 2, 2026, 3:30 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ce8c03480081909120e2f790fe4394 |
completed | April 2, 2026, 3:32 p.m. |
Created at: March 30, 2026, 6:17 p.m.