Triple
T7004921
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alfonso |
E162427
|
entity |
| Predicate | hasFeminineForm |
P1613
|
FINISHED |
| Object | Alfonsina |
E634787
|
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: Alfonsina | Statement: [Alfonso, hasFeminineForm, Alfonsina]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alfonsina Context triple: [Alfonso, hasFeminineForm, Alfonsina]
-
A.
Alfonsa
chosen
Alfonsa is a feminine given name, primarily used in Romance-language cultures, derived from the masculine name Alfonso.
-
B.
Fernanda
Fernanda is a feminine given name commonly used in Romance-language countries, derived from the masculine name Ferdinand.
-
C.
Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
-
D.
María
"María" is a film featuring actress Taryn Power in a significant role.
-
E.
María
María is a feminine given name of Hebrew origin, widely used in Spanish-speaking countries and associated with numerous historical and religious figures.
- 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_69c6885928148190ae31909fbb5e9849 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dc14037c81908bb87250ef29be50 |
completed | March 27, 2026, 7:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7755c7c1481908eed49c72726195e |
completed | March 28, 2026, 6:29 a.m. |
Created at: March 27, 2026, 2:33 p.m.