Triple
T15867167
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Politics of the Basque Country |
E384742
|
entity |
| Predicate | hasEqualSeatAllocationPerProvince |
P120825
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Politics of the Basque Country, hasEqualSeatAllocationPerProvince, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasEqualSeatAllocationPerProvince Context triple: [Politics of the Basque Country, hasEqualSeatAllocationPerProvince, true]
-
A.
seatsAreAllocatedTo
Indicates that specific seats are assigned or designated to particular entities (such as people, groups, or bookings).
-
B.
seatsAllocatedBy
Indicates that seats are assigned or distributed by a particular agent, authority, or mechanism.
-
C.
hasSpecialDelegatesPerProvince
Indicates that a given entity is associated with a specific number or set of special delegates allocated for each province.
-
D.
hasNumberOfProvinces
Indicates the total count of provinces associated with a given entity.
-
E.
regionalSeatAllocationMethod
Indicates the method or rule used to allocate seats within a specific region.
- 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_69d86da4e86481909f1325fdc971b5ec |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e174de2cd48190ab18e48c9f051a2a |
completed | April 16, 2026, 11:46 p.m. |
| PD | Predicate disambiguation | batch_69e142b976c081908d3ba3e705419f3a |
completed | April 16, 2026, 8:12 p.m. |
| PDg | Predicate description generation | batch_69e174da2c2c819099ec46616798245a |
completed | April 16, 2026, 11:46 p.m. |
Created at: April 10, 2026, 4:50 a.m.