Triple
T19214570
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Adolfo |
E480447
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Adolfo (Portuguese) |
—
|
NE NERFINISHED |
How this triple was built (2 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: Adolfo (Portuguese) | Statement: [Adolfo, hasVariant, Adolfo (Portuguese)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Adolfo (Portuguese) Context triple: [Adolfo, hasVariant, Adolfo (Portuguese)]
-
A.
Arturo (Portuguese)
Arturo (Portuguese) is the Portuguese form of the given name Arthur, commonly used in Lusophone countries.
-
B.
Adolfo
chosen
Adolfo is a masculine given name, commonly used in Spanish and Italian, that derives from the Germanic name Adolf.
-
C.
Adelino
Adelino is a masculine given name of Latin origin, commonly used in Portuguese- and Spanish-speaking countries.
-
D.
Adolpho Veloso
Adolpho Veloso is a Brazilian cinematographer known for his visually distinctive work on contemporary films such as the drama "Jockey."
-
E.
Adolpho Lisboa
Adolpho Lisboa was a Brazilian politician who served as mayor of Manaus and is remembered for his role in the city’s development during the rubber boom era.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa397c188190b85bcfd9afd8dce6 |
completed | April 20, 2026, 10:04 a.m. |
Created at: April 10, 2026, 1:22 p.m.