Triple
T9211893
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Machelen |
E221140
|
entity |
| Predicate | hasSubMunicipality |
P747
|
FINISHED |
| Object |
Diegem
Diegem is a village in Flemish Brabant, Belgium, known for its proximity to Brussels Airport and its role as a commercial and office hub.
|
E804672
|
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: Diegem | Statement: [Machelen, hasSubMunicipality, Diegem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Diegem Context triple: [Machelen, hasSubMunicipality, Diegem]
-
A.
Meise
Meise is a municipality in the Belgian province of Flemish Brabant, known for hosting the National Botanic Garden of Belgium.
-
B.
Koekelberg
Koekelberg is a small municipality in the Brussels-Capital Region of Belgium, known for the National Basilica of the Sacred Heart that dominates its skyline.
-
C.
Knokke-Heist
Knokke-Heist is a Belgian coastal resort town known for its beaches, upscale tourism, and proximity to the Dutch border.
-
D.
Bellebeek
Bellebeek is a small stream in Belgium that serves as a right-bank tributary of the River Dender.
-
E.
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.
- 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: Diegem Triple: [Machelen, hasSubMunicipality, Diegem]
Generated description
Diegem is a village in Flemish Brabant, Belgium, known for its proximity to Brussels Airport and its role as a commercial and office hub.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Diegem Target entity description: Diegem is a village in Flemish Brabant, Belgium, known for its proximity to Brussels Airport and its role as a commercial and office hub.
-
A.
Meise
Meise is a municipality in the Belgian province of Flemish Brabant, known for hosting the National Botanic Garden of Belgium.
-
B.
Koekelberg
Koekelberg is a small municipality in the Brussels-Capital Region of Belgium, known for the National Basilica of the Sacred Heart that dominates its skyline.
-
C.
Knokke-Heist
Knokke-Heist is a Belgian coastal resort town known for its beaches, upscale tourism, and proximity to the Dutch border.
-
D.
Bellebeek
Bellebeek is a small stream in Belgium that serves as a right-bank tributary of the River Dender.
-
E.
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.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccd9b69838819088f33ca995fce222 |
completed | April 1, 2026, 8:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d14bc281688190b65b8224b70f08d7 |
completed | April 4, 2026, 5:34 p.m. |
| NEDg | Description generation | batch_69d14ca535588190aa2ff77f0658226b |
completed | April 4, 2026, 5:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d14d29d32c8190a8615b3450492abc |
completed | April 4, 2026, 5:40 p.m. |
Created at: March 30, 2026, 7:27 p.m.