Triple
T19472212
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ikaria regional unit |
E487150
|
entity |
| Predicate | regionSeatType |
P48956
|
FINISHED |
| Object | regional unit seat |
—
|
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: regional unit seat | Statement: [Ikaria regional unit, regionSeatType, regional unit seat]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionSeatType Context triple: [Ikaria regional unit, regionSeatType, regional unit seat]
-
A.
regionSeatsCount
Indicates the number of seats allocated or available within a specific region.
-
B.
regionSeatLocatedIn
Indicates that the administrative seat or capital of a region is located within that region.
-
C.
regionType
Indicates the classification or category of a region, specifying what kind of region it is (e.g., administrative, geographic, or functional).
-
D.
individualSeats
Indicates that an entity provides or consists of separate, single-person seating positions rather than shared or bench-style seating.
-
E.
typicalSeat
chosen
Indicates the usual or standard seating position or location associated with an entity in a given context.
- 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_69d8e8d924388190b847cb15bb3d0aff |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e633e8d9188190a4939f03bad89add |
completed | April 20, 2026, 2:10 p.m. |
| PD | Predicate disambiguation | batch_69e4fd7883308190b73912a71a35a835 |
completed | April 19, 2026, 4:06 p.m. |
Created at: April 10, 2026, 1:39 p.m.