Triple
T1548858
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Caldas Department |
E33040
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object |
Viterbo
Viterbo is a municipality in the Caldas Department of Colombia, known for its coffee production and scenic Andean landscapes.
|
E263323
|
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: Viterbo | Statement: [Caldas Department, hasCity, Viterbo]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Viterbo Context triple: [Caldas Department, hasCity, Viterbo]
-
A.
Viterbo
Viterbo is a historic city in central Italy known for its well-preserved medieval center, ancient thermal baths, and role as a papal residence in the 13th century.
-
B.
Orvieto
Orvieto is a historic hilltop city in Umbria, Italy, renowned for its dramatic cliffside setting and magnificent Gothic cathedral.
-
C.
Perugia
Perugia is a historic hilltop city in central Italy, renowned for its Etruscan heritage, medieval architecture, and vibrant cultural and university life.
-
D.
Pomezia
Pomezia is a modern industrial and residential town in the Lazio region of central Italy, situated just south of Rome.
-
E.
Gubbio
Gubbio is a historic medieval town in the Umbria region of central Italy, known for its well-preserved stone architecture and traditional festivals.
- 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: Viterbo Triple: [Caldas Department, hasCity, Viterbo]
Generated description
Viterbo is a municipality in the Caldas Department of Colombia, known for its coffee production and scenic Andean landscapes.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Viterbo Target entity description: Viterbo is a municipality in the Caldas Department of Colombia, known for its coffee production and scenic Andean landscapes.
-
A.
Viterbo
Viterbo is a historic city in central Italy known for its well-preserved medieval center, ancient thermal baths, and role as a papal residence in the 13th century.
-
B.
Orvieto
Orvieto is a historic hilltop city in Umbria, Italy, renowned for its dramatic cliffside setting and magnificent Gothic cathedral.
-
C.
Perugia
Perugia is a historic hilltop city in central Italy, renowned for its Etruscan heritage, medieval architecture, and vibrant cultural and university life.
-
D.
Pomezia
Pomezia is a modern industrial and residential town in the Lazio region of central Italy, situated just south of Rome.
-
E.
Gubbio
Gubbio is a historic medieval town in the Umbria region of central Italy, known for its well-preserved stone architecture and traditional festivals.
- 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_69a885ee6db8819099502bc5ce8af881 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90856642c81909d88a679eb265b10 |
completed | March 5, 2026, 4:36 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69aeb3a390bc8190891ebd5d8a48d818 |
completed | March 9, 2026, 11:48 a.m. |
| NEDg | Description generation | batch_69aeb48dfbfc81908193c909315bd030 |
completed | March 9, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69aeb57af28c8190bfca30ad3e7ca8b3 |
completed | March 9, 2026, 11:56 a.m. |
Created at: March 4, 2026, 7:26 p.m.