Triple
T19651265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kelvin Herrera |
E471820
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Herrera |
—
|
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: Herrera | Statement: [Kelvin Herrera, familyName, Herrera]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Herrera Context triple: [Kelvin Herrera, familyName, Herrera]
-
A.
Herrera
chosen
Herrera is a common Spanish surname borne by numerous notable figures across sports, politics, arts, and other fields in the Spanish-speaking world.
-
B.
Herrero
Herrero is a Spanish occupational surname derived from the word for "blacksmith" or "smith."
-
C.
Horcajo
Horcajo is a residential neighborhood within the Moratalaz district of Madrid, Spain.
-
D.
De Herrera
De Herrera is a Spanish surname, often associated with noble lineages and historical figures from Spain and Latin America.
-
E.
Vásquez
Vásquez is a Spanish-language surname common in Latin America and Spain, borne by numerous notable figures in sports, politics, and the arts.
- 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_69d8e51395348190ac1416d46dfc6db0 |
completed | April 10, 2026, 11:54 a.m. |
| NER | Named-entity recognition | batch_69e64141f61c819098fe148581fb1747 |
completed | April 20, 2026, 3:07 p.m. |
Created at: April 10, 2026, 1:44 p.m.