Triple
T17019467
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jurbarkas |
E412906
|
entity |
| Predicate | roadDistanceTo |
P7750
|
FINISHED |
| Object | Šilutė |
—
|
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: Šilutė | Statement: [Jurbarkas, roadDistanceTo, Šilutė]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Šilutė Context triple: [Jurbarkas, roadDistanceTo, Šilutė]
-
A.
Šilutė
chosen
Šilutė is a town in western Lithuania known for its location near the Nemunas River delta and its historical ties to the former East Prussian region.
-
B.
Ukmergė
Ukmergė is a historic town in central Lithuania known for its location along the Šventoji River and its role as a regional cultural and administrative center.
-
C.
Radviliškis
Radviliškis is a town in northern Lithuania known as a regional railway hub and administrative center within Šiauliai County.
-
D.
Joniškis
Joniškis is a small town in northern Lithuania known for its historic architecture and cultural heritage, including well-preserved synagogues.
-
E.
Zarasai
Zarasai is a small town in northeastern Lithuania known for its lakes and scenic natural surroundings.
- 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_69d886cc4170819093deddc7b8b4b6a7 |
completed | April 10, 2026, 5:12 a.m. |
| NER | Named-entity recognition | batch_69e3d481a0988190a13d0928e0c7ebbf |
completed | April 18, 2026, 6:59 p.m. |
Created at: April 10, 2026, 5:33 a.m.