Triple
T22247987
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Member of the Chamber of Representatives of Belgium |
E549893
|
entity |
| Predicate | hasNumberOfSeatsInChamber |
P73787
|
FINISHED |
| Object | 150 |
—
|
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: 150 | Statement: [Member of the Chamber of Representatives of Belgium, hasNumberOfSeatsInChamber, 150]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfSeatsInChamber Context triple: [Member of the Chamber of Representatives of Belgium, hasNumberOfSeatsInChamber, 150]
-
A.
hasLegislativeChamberSeats
chosen
Indicates that a legislative chamber holds or is allocated a specified number of seats.
-
B.
hasChairCount
Indicates the number of chairs associated with a given entity.
-
C.
numberOfPartiesInChamber
Indicates the total count of distinct parties represented within a specific legislative chamber.
-
D.
hasSeatsForConstituency
Indicates that a governing body or institution allocates or provides a certain number of representative seats for a specific constituency.
-
E.
hasChambersFor
Indicates that one entity contains or provides designated chambers or compartments intended for use by another 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_69e11e41d9408190bd770cf282e22753 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f13219dc3481908dd987c4e98623e6 |
completed | April 28, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69e72fe1e0cc8190bd13cff2a0846225 |
completed | April 21, 2026, 8:05 a.m. |
Created at: April 16, 2026, 8:38 p.m.