Triple
T12096639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of the Republic of Peru |
E288087
|
entity |
| Predicate | style |
P87
|
FINISHED |
| Object | Señor Presidente |
E49686
|
NE FINISHED |
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: Señor Presidente | Statement: [President of the Republic of Peru, style, Señor Presidente]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Señor Presidente Context triple: [President of the Republic of Peru, style, Señor Presidente]
-
A.
Señor Presidente
chosen
Señor Presidente is the formal Spanish honorific used to address the sitting President of Mexico.
-
B.
Señor Vicepresidente
Señor Vicepresidente is the formal Spanish honorific used to address the Second Vice President of Peru.
-
C.
Compañero Presidente
Compañero Presidente is an honorific style used to address the President of the Republic of Cuba, reflecting the country’s socialist and egalitarian political culture.
-
D.
Señor Ministro
Señor Ministro is the formal honorific style used to address the Minister of National Defense of Chile.
-
E.
Mr President
Mr President is the formal style of address used for the head of state of Poland.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab4964708190850585628b287b0c |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91552645c81909aff601ab3d3c0e6 |
completed | April 10, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f5f6708f8881909c4f40a466bc0acf |
completed | May 2, 2026, 1:04 p.m. |
Created at: April 8, 2026, 9:48 p.m.