Triple
T21296779
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | João Carlos of Brazil |
E524944
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | João |
—
|
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: João | Statement: [João Carlos of Brazil, givenName, João]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: João Context triple: [João Carlos of Brazil, givenName, João]
-
A.
João
chosen
João is a common Portuguese male given name widely used in Portuguese-speaking countries, equivalent to "John" in English.
-
B.
João dos Santos
João dos Santos is a notable individual distinguished enough to be recognized as a prominent bearer of the surname Santos.
-
C.
João Miguel
João Miguel is a 1932 social realist novel by Brazilian author Rachel de Queiroz that portrays the harsh lives and moral struggles of Brazil’s rural poor.
-
D.
Sebastião
Sebastião is the Portuguese variant of the given name Sebastian, commonly used in Portuguese-speaking countries.
-
E.
Antônio
Antônio is the given name of the Brazilian artist known professionally as Tunga, a prominent figure in contemporary sculpture and installation art.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7385968308190bc9fe5c2bd4598e6 |
completed | April 21, 2026, 8:42 a.m. |
Created at: April 16, 2026, 4:04 p.m.