Triple
T34268627
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Constituency Warsaw |
E879243
|
entity |
| Predicate | hasSeatAllocationMethod |
P200903
|
FINISHED |
| Object | D’Hondt method |
—
|
NE NERFINISHED |
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: D’Hondt method | Statement: [Constituency Warsaw, hasSeatAllocationMethod, D’Hondt method]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSeatAllocationMethod Context triple: [Constituency Warsaw, hasSeatAllocationMethod, D’Hondt method]
-
A.
hasSeatAllocation
Indicates that an entity has been assigned a specific seat or set of seats within a defined seating arrangement or capacity.
-
B.
hasSeatStatus
Indicates the current condition or availability state of a seat in a given context.
-
C.
seatsAreAllocatedTo
Indicates that specific seats are assigned or designated to particular entities (such as people, groups, or bookings).
-
D.
hasReservedSeats
Indicates that specific seats have been set aside or allocated in advance for a particular entity or purpose.
-
E.
seatAllocationLevel
Indicates the degree or priority level at which seats are assigned or distributed among eligible entities.
- F. None of above. chosen
Provenance (4 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_69f349b4f5fc819094b441d18e95e5f1 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_69ffb82be8148190a1c870d467a28c80 |
completed | May 9, 2026, 10:41 p.m. |
| PD | Predicate disambiguation | batch_69ffb7bbd550819094052e9a0d0ae320 |
completed | May 9, 2026, 10:39 p.m. |
| PDg | Predicate description generation | batch_69ffb82b40788190bf401344a3f288dd |
completed | May 9, 2026, 10:41 p.m. |
Created at: May 1, 2026, 1:56 a.m.