Triple
T7076247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Francisca González Mateos |
E164826
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Francisca |
E88236
|
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: Francisca | Statement: [Francisca González Mateos, givenName, Francisca]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Francisca Context triple: [Francisca González Mateos, givenName, Francisca]
-
A.
Francisca
chosen
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
-
B.
Alfonsa
Alfonsa is a feminine given name, primarily used in Romance-language cultures, derived from the masculine name Alfonso.
-
C.
María
María is a key character in Ernest Hemingway's novel "For Whom the Bell Tolls," known as a young Spanish woman and love interest of the protagonist amid the Spanish Civil War.
-
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_69c6887cbc6c8190bdfac42d940f4d8a |
completed | March 27, 2026, 1:39 p.m. |
| NER | Named-entity recognition | batch_69c6e4ebf4048190bf5d7156817f93a7 |
completed | March 27, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c79468c7688190bf10433f05e77574 |
completed | March 28, 2026, 8:42 a.m. |
Created at: March 27, 2026, 2:40 p.m.