Triple
T3225691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lünen |
E67615
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Panevėžys |
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: Panevėžys | Statement: [Lünen, hasTwinTown, Panevėžys]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Panevėžys Context triple: [Lünen, hasTwinTown, Panevėžys]
-
A.
Panevėžys
chosen
Panevėžys is a major city in northern Lithuania known as an important regional industrial and cultural center.
-
B.
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.
-
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.
Švenčionys
Švenčionys is a small historic town in eastern Lithuania known for its multicultural past and former Jewish community.
-
E.
Vilnius
Vilnius is the capital and largest city of Lithuania, known for its well-preserved medieval Old Town and rich cultural and historical heritage.
- 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_69ad858c61888190a31196310d9b30b5 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaeb4cd3481908af8a2c9b6c0742d |
completed | March 8, 2026, 5:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2625eaa708190b23ca6e575d664a2 |
completed | March 12, 2026, 6:51 a.m. |
Created at: March 8, 2026, 3:08 p.m.