Triple
T19991364
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lower Styria |
E494071
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object | Velenje |
—
|
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: Velenje | Statement: [Lower Styria, containsTown, Velenje]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Velenje Context triple: [Lower Styria, containsTown, Velenje]
-
A.
Velenje
chosen
Velenje is a modern industrial town in northern Slovenia known for its coal mining heritage, large lakeside recreational area, and one of the largest Tito statues in the world.
-
B.
Kranj
Kranj is a historic industrial city in northwestern Slovenia, known as a regional economic center and gateway to the Slovenian Alps.
-
C.
Maribor
Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
-
D.
Celje
Celje is a historic city in eastern Slovenia known for its medieval castle and former prominence as a regional political and economic center.
-
E.
Sevnica
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65fe00b908190bda6b9a3a3281ec0 |
completed | April 20, 2026, 5:18 p.m. |
Created at: April 11, 2026, 3:31 p.m.