Triple
T21368900
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | E40 motorway |
E526996
|
entity |
| Predicate | passesThroughCity |
P416
|
FINISHED |
| Object | Luhansk |
—
|
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: Luhansk | Statement: [E40 motorway, passesThroughCity, Luhansk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Luhansk Context triple: [E40 motorway, passesThroughCity, Luhansk]
-
A.
Luhansk
chosen
Luhansk is a major city in eastern Ukraine, historically an industrial center and currently a focal point in the Russo-Ukrainian conflict.
-
B.
Luhansk Oblast
Luhansk Oblast is an eastern Ukrainian region that forms part of the industrial Donbas area and has been a focal point of the Russo-Ukrainian conflict.
-
C.
Donetsk Oblast
Donetsk Oblast is an industrial and heavily urbanized region in eastern Ukraine, historically known for coal mining and metallurgy and currently a focal point of the Russo-Ukrainian conflict.
-
D.
Donetsk
Donetsk is a major industrial city in eastern Ukraine, historically known for its coal mining and steel production.
-
E.
Zaporizhzhia
Zaporizhzhia is a major industrial city in southeastern Ukraine, known for its large hydroelectric power plant on the Dnieper River and its significant role in the country’s energy and manufacturing sectors.
- 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_69e0b51e80808190ba5cb05667af02a9 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69ee5baf5fb4819093f8d8afdd83ffdb |
completed | April 26, 2026, 6:38 p.m. |
Created at: April 16, 2026, 5:09 p.m.