Triple
T2820438
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chemnitz–Leipzig railway |
E54793
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object | Borna |
E185203
|
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: Borna | Statement: [Chemnitz–Leipzig railway, passesThrough, Borna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Borna Context triple: [Chemnitz–Leipzig railway, passesThrough, Borna]
-
A.
Borna
chosen
Borna is a town in the German state of Saxony that serves as an administrative and economic center in the Leipzig region.
-
B.
Čukarica
Čukarica is a municipality of Belgrade known for its mix of urban neighborhoods, industrial zones, and green areas along the Sava River.
-
C.
Barajevo
Barajevo is a suburban municipality of Belgrade, Serbia, located in the southern part of the city’s administrative area.
-
D.
Obrenovac
Obrenovac is a suburban municipality of Belgrade in Serbia, known for its industrial facilities and proximity to the Sava River.
-
E.
Perovo
Perovo is a Moscow Metro station serving the Kalininsko–Solntsevskaya Line in the Perovo District of eastern Moscow.
- 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_69ab49e100c0819082a40cb797383243 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abde6e85008190a08eb2bf8e393e7e |
completed | March 7, 2026, 8:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afcea809e48190b22f25a3c8c1acdd |
completed | March 10, 2026, 7:56 a.m. |
Created at: March 6, 2026, 9:59 p.m.