Triple
T18307486
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaunas District Municipality |
E438525
|
entity |
| Predicate | hasAdministrativeCentre |
P1474
|
FINISHED |
| Object | Kaunas |
—
|
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: Kaunas | Statement: [Kaunas District Municipality, hasAdministrativeCentre, Kaunas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kaunas Context triple: [Kaunas District Municipality, hasAdministrativeCentre, Kaunas]
-
A.
Kaunas
chosen
Kaunas is the second-largest city in Lithuania, known as a historic cultural and academic center located at the confluence of the Nemunas and Neris rivers.
-
B.
Klaipėda
Klaipėda is a Lithuanian port city on the Baltic Sea known as the country’s main maritime gateway and a key regional transport and industrial hub.
-
C.
Vilnius
Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
-
D.
Tauragė
Tauragė is a town in western Lithuania known as an administrative, cultural, and economic center of the surrounding region.
-
E.
Panevėžys
Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b915e3e881909125d760c15d0c29 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e5021519a481908a9b6561946f1c65 |
completed | April 19, 2026, 4:25 p.m. |
Created at: April 10, 2026, 10:35 a.m.