Triple
T1900162
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brussels-Capital Region |
E37672
|
entity |
| Predicate | numberOfMunicipalities |
P30910
|
FINISHED |
| Object | 19 |
—
|
LITERAL FINISHED |
How this triple was built (2 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: 19 | Statement: [Brussels-Capital Region, numberOfMunicipalities, 19]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfMunicipalities Context triple: [Brussels-Capital Region, numberOfMunicipalities, 19]
-
A.
hasNumberOfMunicipalities
chosen
Indicates the relationship that specifies how many municipalities are associated with or contained within a given administrative or geographic entity.
-
B.
numberOfRegionalCouncils
Indicates the total count of regional councils associated with a given entity.
-
C.
numberOfDistricts
Indicates the total count of districts associated with a given entity or area.
-
D.
numberOfParishes
Indicates the total count of parishes associated with a given entity.
-
E.
hasNumberOfCounties
Indicates the relationship that specifies how many counties are associated with or contained within a given entity.
- F. None of above.
Provenance (3 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_69a8861be7148190a680937ec451a304 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb18c46c88190b10c05bf5c6a2d9c |
completed | March 7, 2026, 5:03 a.m. |
| PD | Predicate disambiguation | batch_69abafe7e7e88190b58c0df59187c0c2 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:35 p.m.