Triple
T21119464
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pêpê Rapazote |
E520388
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Pêpê Rapazote |
—
|
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: Pêpê Rapazote | Statement: [Pêpê Rapazote, name, Pêpê Rapazote]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pêpê Rapazote Context triple: [Pêpê Rapazote, name, Pêpê Rapazote]
-
A.
Pêpê Rapazote
chosen
Pêpê Rapazote is a Portuguese actor known for his work in international film and television, including roles in series like "Narcos" and various European productions.
-
B.
Niño
Niño is a Spanish surname commonly borne by individuals and families in Spanish-speaking countries.
-
C.
Bambito
Bambito is a small mountain village in Panama known for its cool climate, lush highland scenery, and proximity to popular ecotourism destinations.
-
D.
Bebeto
Bebeto is a retired Brazilian footballer and prolific striker best known for his successful international career with Brazil, including winning the 1994 FIFA World Cup.
-
E.
Budak
Budak is a Turkish surname borne by various individuals, including academics, politicians, and public figures.
- 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_69e0b50a623881909c0bbaf4f2c055e7 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e7223176c48190bfbaea41c2209a15 |
completed | April 21, 2026, 7:07 a.m. |
Created at: April 16, 2026, 2:55 p.m.