Triple
T1970597
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Armand |
E42789
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Armando |
E138127
|
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: Armando | Statement: [Armand, hasVariant, Armando]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Armando Context triple: [Armand, hasVariant, Armando]
-
A.
Armando
chosen
Armando is a masculine given name of Spanish and Portuguese origin, commonly used in many Spanish-speaking countries.
-
B.
Armando Diaz
Armando Diaz was an Italian general best known for leading Italy to victory on the Italian Front during World War I, particularly at the Battle of Vittorio Veneto.
-
C.
Ernesto
Ernesto is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
D.
Roberto
Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
-
E.
Emilio
Emilio is a masculine given name of Spanish and Italian origin, borne by various notable figures including military leaders, artists, and politicians.
- 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_69a88711151c8190940b2572095059d7 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3d2836c8190a35cb6d8e2dd4bdf |
completed | March 7, 2026, 5:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae95eb4db481909ab7507147f1d674 |
completed | March 9, 2026, 9:42 a.m. |
Created at: March 4, 2026, 7:36 p.m.