Triple
T23130797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rolando Blackman |
E577160
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Rolando |
—
|
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: Rolando | Statement: [Rolando Blackman, givenName, Rolando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Rolando Context triple: [Rolando Blackman, givenName, Rolando]
-
A.
Rolando
chosen
Rolando is a masculine given name, commonly used in Romance-language countries, that is a variant of the name Orlando/Roland.
-
B.
Rollán
Rollán is the Spanish family name of actress Maribel Verdú, known for her prominent roles in Spanish and international cinema.
-
C.
Aroldo
Aroldo is a lesser-known opera by Italian composer Giuseppe Verdi, adapted from his earlier work Stiffelio and set in medieval England and Scotland.
-
D.
Amarildo
Amarildo is a former Brazilian footballer best known as a forward who starred for Botafogo and the Brazil national team in the early 1960s.
-
E.
Lisardo
Lisardo is a Mexican actor and singer known for his roles in telenovelas and television series.
- 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_69e245f7b0e481909c473ff4e6a54e2c |
completed | April 17, 2026, 2:38 p.m. |
| NER | Named-entity recognition | batch_69f18e87cd188190b466f7a4c9670e56 |
completed | April 29, 2026, 4:52 a.m. |
Created at: April 17, 2026, 4 p.m.