Triple
T10285267
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Louise Fazenda |
E241210
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Fazenda
Fazenda is a Portuguese and Spanish surname most notably associated with American silent film comedian Louise Fazenda.
|
E852662
|
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: Fazenda | Statement: [Louise Fazenda, familyName, Fazenda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fazenda Context triple: [Louise Fazenda, familyName, Fazenda]
-
A.
Engenho do Meio
Engenho do Meio is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
-
B.
Feira Nova
Feira Nova is a small municipality located in the semi-arid interior region of the state of Sergipe, Brazil.
-
C.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
D.
Engenho de Dentro
Engenho de Dentro is a neighborhood in Rio de Janeiro, Brazil, known for hosting the Estádio Nilton Santos football stadium.
-
E.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
- 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: Fazenda Triple: [Louise Fazenda, familyName, Fazenda]
Generated description
Fazenda is a Portuguese and Spanish surname most notably associated with American silent film comedian Louise Fazenda.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Fazenda Target entity description: Fazenda is a Portuguese and Spanish surname most notably associated with American silent film comedian Louise Fazenda.
-
A.
Engenho do Meio
Engenho do Meio is a neighborhood located in the city of Recife, in the state of Pernambuco, Brazil.
-
B.
Feira Nova
Feira Nova is a small municipality located in the semi-arid interior region of the state of Sergipe, Brazil.
-
C.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
D.
Engenho de Dentro
Engenho de Dentro is a neighborhood in Rio de Janeiro, Brazil, known for hosting the Estádio Nilton Santos football stadium.
-
E.
Cabaceiras
Cabaceiras is a historic town in the Brazilian state of Paraíba, known for its well-preserved colonial architecture and frequent use as a filming location for movies and television.
- 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_69d381aaafc08190af475ef58dc16aba |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4d2b737788190bfadd0d48ad38f5b |
completed | April 7, 2026, 9:47 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d6f8444c48819095100c6d1d45ccc7 |
completed | April 9, 2026, 12:52 a.m. |
| NEDg | Description generation | batch_69d6fcae243c819095a2e791716805bd |
completed | April 9, 2026, 1:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d6fd3495fc8190a093d2536cfbe58a |
completed | April 9, 2026, 1:13 a.m. |
Created at: April 6, 2026, 11:40 a.m.