Triple
T8937985
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin-Lichtenberg |
E212823
|
entity |
| Predicate | hasTwinTown |
P919
|
FINISHED |
| Object | Jurbarkas |
E412906
|
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: Jurbarkas | Statement: [Berlin-Lichtenberg, hasTwinTown, Jurbarkas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jurbarkas Context triple: [Berlin-Lichtenberg, hasTwinTown, Jurbarkas]
-
A.
Jurbarkas
chosen
Jurbarkas is a small Lithuanian town situated on the banks of the Nemunas River, known for its historical significance and scenic surroundings.
-
B.
Balvi
Balvi is a small town in eastern Latvia that serves as an important local administrative, cultural, and economic center in the Latgale region.
-
C.
Garliava
Garliava is a small town in central Lithuania known as a suburban community near the city of Kaunas.
-
D.
Taurog
Taurog is a surname most notably associated with Norman Taurog, the Academy Award–winning American film director.
-
E.
Horki
Horki is a town in eastern Belarus known for its agricultural academy and regional administrative significance.
- 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_69ca839694c88190b324ffeb43d23b08 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc66b57a348190979effe4f9998eb7 |
completed | April 1, 2026, 12:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc1e692548190b631c4926927d12f |
completed | April 3, 2026, 1:34 p.m. |
Created at: March 30, 2026, 6:58 p.m.