Triple
T3945732
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kaunas Railway Station |
E92141
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Šiauliai |
E94346
|
NE 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: Šiauliai | Statement: [Kaunas Railway Station, connectsTo, Šiauliai]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Šiauliai Context triple: [Kaunas Railway Station, connectsTo, Šiauliai]
-
A.
Švenčionys
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
-
B.
Panevėžys
chosen
Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
-
C.
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.
-
D.
Kaunas
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.
-
E.
Alytus
Alytus is a city in southern Lithuania known as a regional cultural and economic center on the banks of the Nemunas River.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed965502c8190904ebad1203a4ae8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aef0d8841081908d2c1de8e5758017 |
completed | March 9, 2026, 4:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b57f10d1008190a3bbe93c1215a244 |
completed | March 14, 2026, 3:30 p.m. |
Created at: March 9, 2026, 3:24 p.m.