Triple
T23335867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pacífica Fernández Oreamuno |
E591581
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Fernández |
—
|
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: Fernández | Statement: [Pacífica Fernández Oreamuno, familyName, Fernández]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Fernández Context triple: [Pacífica Fernández Oreamuno, familyName, Fernández]
-
A.
Fernández
chosen
Fernández is a common Spanish-language surname widely used across Spain and Latin America.
-
B.
Fernández Vial
Fernández Vial is a Chilean football club based in Concepción, known for its historic roots and passionate local support.
-
C.
Duhalde
Duhalde is a prominent Argentine political surname most notably associated with Eduardo Duhalde, a former president of Argentina.
-
D.
Rosas
Rosas is a residential neighborhood in the San Blas-Canillejas district of Madrid, Spain, known for its local amenities and urban character.
-
E.
Pérez
Pérez is a common Spanish-language surname widely found in Spain and Latin America.
- 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_69e25d20156c81908c5c53195bd9c738 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f197f1e0588190bf073b92be0bf9e4 |
completed | April 29, 2026, 5:32 a.m. |
Created at: April 17, 2026, 5:16 p.m.