Triple
T8105256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | President of Argentina |
E189210
|
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 Argentina, style, Señor Presidente]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Señor Presidente Context triple: [President of Argentina, 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.
Mr President
Mr President is the formal style of address used for the head of state of Poland.
-
E.
Mr. President
"Mr. President" is the formal spoken address traditionally used for the sitting President of the United States.
- 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_69ca82b9d5848190a24672775d5c5011 |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb42c0ce6481909887be82c8019383 |
completed | March 31, 2026, 3:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc6422fb4c8190a5e7bedd323241d7 |
completed | April 1, 2026, 12:17 a.m. |
Created at: March 30, 2026, 5:31 p.m.