Triple
T16404110
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Masaya Department |
E398379
|
entity |
| Predicate | hasMunicipality |
P847
|
FINISHED |
| Object |
Catarina
Catarina is a small Nicaraguan town and municipality known for its scenic views over Laguna de Apoyo and its traditional plant and craft markets.
|
E1215564
|
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: Catarina | Statement: [Masaya Department, hasMunicipality, Catarina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Catarina Context triple: [Masaya Department, hasMunicipality, Catarina]
-
A.
Catharina
Catharina of Württemberg was a 19th-century German princess who became Queen consort of Westphalia through her marriage to Jérôme Bonaparte, Napoleon’s youngest brother.
-
B.
Catharina
Catharina is a feminine given name of Greek origin, commonly used in various European cultures and often associated with historical and religious figures.
-
C.
Caterina
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
D.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
E.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
- 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: Catarina Triple: [Masaya Department, hasMunicipality, Catarina]
Generated description
Catarina is a small Nicaraguan town and municipality known for its scenic views over Laguna de Apoyo and its traditional plant and craft markets.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Catarina Target entity description: Catarina is a small Nicaraguan town and municipality known for its scenic views over Laguna de Apoyo and its traditional plant and craft markets.
-
A.
Catharina
Catharina of Württemberg was a 19th-century German princess who became Queen consort of Westphalia through her marriage to Jérôme Bonaparte, Napoleon’s youngest brother.
-
B.
Catharina
Catharina is a feminine given name of Greek origin, commonly used in various European cultures and often associated with historical and religious figures.
-
C.
Caterina
Caterina is an Italian given name, equivalent to Catherine, commonly used for women in Italian-speaking and related cultures.
-
D.
Luisa
Luisa is a feminine given name used in various languages, particularly Romance languages, as a form of the name Louise.
-
E.
Rosana
Rosana is a municipality in the state of São Paulo, Brazil, known for hosting a campus of São Paulo State University (UNESP).
- 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_69d87f2950248190bc8ad9b9bebdc8c8 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e327d12dc08190a5b497692b667ed7 |
completed | April 18, 2026, 6:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a004f439d048190bf779cb263b7c7a7 |
completed | May 10, 2026, 9:26 a.m. |
| NEDg | Description generation | batch_6a0053190a1481909af0c9ac78f70188 |
completed | May 10, 2026, 9:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00537072208190bb1a42a8f0fff3f5 |
completed | May 10, 2026, 9:44 a.m. |
Created at: April 10, 2026, 5:09 a.m.