Triple
T3333089
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dane County |
E70077
|
entity |
| Predicate | containsTown |
P847
|
FINISHED |
| Object |
Verona (town)
Verona is a suburban town in south-central Wisconsin, United States, known for its proximity to Madison and its mix of residential communities, parks, and local businesses.
|
E349424
|
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: Verona (town) | Statement: [Dane County, containsTown, Verona (town)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Verona (town) Context triple: [Dane County, containsTown, Verona (town)]
-
A.
Verona
Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
-
B.
Vicenza
Vicenza is a historic city in northeastern Italy renowned for its Palladian architecture and cultural heritage.
-
C.
Veron
Veron is a microbiologist credited with formally naming and classifying the bacterial genus Campylobacter.
-
D.
Treviso
Treviso is a historic city in northeastern Italy’s Veneto region, known for its medieval walls, canals, and proximity to Venice.
-
E.
Padua
Padua is a historic city in northern Italy renowned as a major cultural and academic center, home to one of Europe’s oldest universities.
- 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: Verona (town) Triple: [Dane County, containsTown, Verona (town)]
Generated description
Verona is a suburban town in south-central Wisconsin, United States, known for its proximity to Madison and its mix of residential communities, parks, and local businesses.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Verona (town) Target entity description: Verona is a suburban town in south-central Wisconsin, United States, known for its proximity to Madison and its mix of residential communities, parks, and local businesses.
-
A.
Verona
Verona is a historic city in northern Italy renowned for its well-preserved Roman architecture and its association with Shakespeare’s "Romeo and Juliet."
-
B.
Vicenza
Vicenza is a historic city in northeastern Italy renowned for its Palladian architecture and cultural heritage.
-
C.
Veron
Veron is a microbiologist credited with formally naming and classifying the bacterial genus Campylobacter.
-
D.
Treviso
Treviso is a historic city in northeastern Italy’s Veneto region, known for its medieval walls, canals, and proximity to Venice.
-
E.
Padua
Padua is a historic city in northern Italy renowned as a major cultural and academic center, home to one of Europe’s oldest universities.
- 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_69ad85a24f208190bcf83131bfed3521 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb194960081909333c855f06d8b03 |
completed | March 8, 2026, 5:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b31a867cac81909ddde955c1752ab8 |
completed | March 12, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69b31c393f20819098d5761372d6a980 |
completed | March 12, 2026, 8:04 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69b3206be2748190874560701dc1ed18 |
completed | March 12, 2026, 8:22 p.m. |
Created at: March 8, 2026, 3:12 p.m.