Triple
T26938302
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Central Sava statistical region |
E678443
|
entity |
| Predicate | europeanUnionRegion |
P162370
|
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: [Central Sava statistical region, europeanUnionRegion, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: europeanUnionRegion Context triple: [Central Sava statistical region, europeanUnionRegion, true]
-
A.
europeanRegion
Indicates that an entity is located in, associated with, or classified as part of a region within Europe.
-
B.
EuropeanAffiliation
Indicates that an entity is associated with Europe, such as by membership, origin, operation, or jurisdiction within European countries or institutions.
-
C.
ITURegion
Indicates the International Telecommunication Union (ITU) regional classification to which an entity (such as a location or station) belongs.
-
D.
regionalBloc
Indicates that an entity is a member of, or associated with, a specific regional political or economic bloc.
-
E.
eurozoneMember
Indicates that an entity is a country that belongs to the Eurozone, i.e., it uses the euro as its official currency within the European Union’s monetary union.
- 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_69eeeb4d69588190a7c912164a1c37b3 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f6250321088190ae3ed1dc9f2fcd03 |
completed | May 2, 2026, 4:23 p.m. |
| PD | Predicate disambiguation | batch_69f623a7539c8190b71797f583da9f63 |
completed | May 2, 2026, 4:17 p.m. |
| PDg | Predicate description generation | batch_69f62473a38481909b919f88ffb5b492 |
completed | May 2, 2026, 4:21 p.m. |
Created at: April 27, 2026, 6:17 a.m.