Triple
T9437347
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rivadavia Street |
E227544
|
entity |
| Predicate | runsThrough |
P416
|
FINISHED |
| Object |
Floresta
Floresta is a traditional residential neighborhood in western Buenos Aires, Argentina, known for its historic architecture and strong local community.
|
E799763
|
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: Floresta | Statement: [Rivadavia Street, runsThrough, Floresta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Floresta Context triple: [Rivadavia Street, runsThrough, Floresta]
-
A.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
B.
Ourinhos
Ourinhos is a municipality in the southwestern part of the state of São Paulo, Brazil, known as a regional commercial and agricultural center.
-
C.
Tamarineira
Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
-
D.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
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: Floresta Triple: [Rivadavia Street, runsThrough, Floresta]
Generated description
Floresta is a traditional residential neighborhood in western Buenos Aires, Argentina, known for its historic architecture and strong local community.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Floresta Target entity description: Floresta is a traditional residential neighborhood in western Buenos Aires, Argentina, known for its historic architecture and strong local community.
-
A.
Caieiras
Caieiras is a municipality in the metropolitan region of São Paulo, Brazil, known for its industrial activity and surrounding green areas.
-
B.
Ourinhos
Ourinhos is a municipality in the southwestern part of the state of São Paulo, Brazil, known as a regional commercial and agricultural center.
-
C.
Tamarineira
Tamarineira is a neighborhood in the Brazilian city of Recife, known for its residential areas and local commerce.
-
D.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
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_69ca8437a7ac81908651de48f2d2141d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd7edff5e881909b72976e8909ba4b |
completed | April 1, 2026, 8:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d11053d8008190a29575149d2e027f |
completed | April 4, 2026, 1:21 p.m. |
| NEDg | Description generation | batch_69d111a770c881909a2902d36cd7913c |
completed | April 4, 2026, 1:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69d112634fb48190b4c7e9d997d27928 |
completed | April 4, 2026, 1:30 p.m. |
Created at: March 30, 2026, 7:50 p.m.