Triple
T7783542
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pesaro |
E187184
|
entity |
| Predicate | twinnedWith |
P1072
|
FINISHED |
| Object |
Kranj
Kranj is a historic industrial city in northwestern Slovenia, known as a regional economic center and gateway to the Slovenian Alps.
|
E693451
|
NE FINISHED |
How this triple was built (4 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: Kranj | Statement: [Pesaro, twinnedWith, Kranj]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kranj Context triple: [Pesaro, twinnedWith, Kranj]
-
A.
Velenje
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.
Maribor
Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
-
C.
Celje
Celje is a historic city in eastern Slovenia known for its medieval castle and former prominence as a regional political and economic center.
-
D.
Sevnica
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
-
E.
Ljubljana
Ljubljana is the capital and largest city of Slovenia, known for its picturesque old town, Baroque and Art Nouveau architecture, and vibrant cultural scene along the Ljubljanica River.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Kranj Triple: [Pesaro, twinnedWith, Kranj]
Generated description
Kranj is a historic industrial city in northwestern Slovenia, known as a regional economic center and gateway to the Slovenian Alps.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kranj Target entity description: Kranj is a historic industrial city in northwestern Slovenia, known as a regional economic center and gateway to the Slovenian Alps.
-
A.
Velenje
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.
Maribor
Maribor is Slovenia’s second-largest city, known for its historic old town, wine culture, and the world’s oldest grapevine.
-
C.
Celje
Celje is a historic city in eastern Slovenia known for its medieval castle and former prominence as a regional political and economic center.
-
D.
Sevnica
Sevnica is a small town in central Slovenia known as the childhood home of former U.S. First Lady Melania Trump.
-
E.
Ljubljana
Ljubljana is the capital and largest city of Slovenia, known for its picturesque old town, Baroque and Art Nouveau architecture, and vibrant cultural scene along the Ljubljanica River.
- F. None of above. chosen
Provenance (5 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_69ca82af2d2c8190963861f5e0b8bf21 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cadf1f9c648190ac2b06d0d54035ea |
completed | March 30, 2026, 8:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69caf5e400d881909d6cdeb7eaac3a59 |
completed | March 30, 2026, 10:15 p.m. |
| NEDg | Description generation | batch_69caf81ebde881909bd131da8987b449 |
completed | March 30, 2026, 10:24 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cafa013f348190a2067dee4a0c8c40 |
completed | March 30, 2026, 10:32 p.m. |
Created at: March 30, 2026, 4:22 p.m.