Triple
T3506129
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | BR |
E74078
|
entity |
| Predicate | hasSeatAllocationPrinciple |
P9552
|
FINISHED |
| Object | based on population of the federal states |
—
|
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: based on population of the federal states | Statement: [BR, hasSeatAllocationPrinciple, based on population of the federal states]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatAllocationPrinciple Context triple: [BR, hasSeatAllocationPrinciple, based on population of the federal states]
-
A.
seatsAllocatedBy
Indicates that seats are assigned or distributed by a particular agent, authority, or mechanism.
-
B.
seatSelectionPolicy
chosen
Indicates the rules or constraints governing how seats are chosen or assigned in a given context.
-
C.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
D.
seatNotationSystem
Indicates the system or convention used to label, number, or otherwise denote seats within a venue or vehicle.
-
E.
remainingSeatsAllocation
Indicates how any seats that are still unassigned after an initial distribution are allocated among eligible parties or entities.
- 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_69ad85ce7a9c81909ddc5cf0cb67a6e3 |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbbf38e988190998d722b95830411 |
completed | March 8, 2026, 6:12 p.m. |
| PD | Predicate disambiguation | batch_69adae0e770481908528fa35eda53003 |
completed | March 8, 2026, 5:12 p.m. |
Created at: March 8, 2026, 3:18 p.m.