Triple
T17435279
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugenio Derbez |
E423985
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Eugenio |
—
|
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: Eugenio | Statement: [Eugenio Derbez, givenName, Eugenio]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugenio Context triple: [Eugenio Derbez, givenName, Eugenio]
-
A.
Eugenio
chosen
Eugenio is a masculine given name of Greek origin, commonly used in Spanish- and Italian-speaking countries.
-
B.
Ernesto
Ernesto is a masculine given name of Spanish origin commonly used in Spanish-speaking countries.
-
C.
Elicio
Elicio is a shepherd and one of the principal pastoral protagonists in Miguel de Cervantes’ early novel "La Galatea."
-
D.
Eduardo
Eduardo is a masculine given name commonly used in Spanish and Portuguese-speaking countries, equivalent to the English name Edward.
-
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 (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_69d889d88b6081908bada047f5b3ba51 |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e4490361c081908fd24f9a812f212c |
completed | April 19, 2026, 3:16 a.m. |
Created at: April 10, 2026, 5:46 a.m.