Triple
T3756799
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Corregidora |
E82066
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Josefa |
E91111
|
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: Josefa | Statement: [La Corregidora, givenName, Josefa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Josefa Context triple: [La Corregidora, givenName, Josefa]
-
A.
Josefa
chosen
Josefa is a feminine given name of Spanish origin, historically borne by notable figures such as Mexican independence heroine Josefa Ortiz de Domínguez.
-
B.
Francisca
Francisca is a feminine given name, used in various European and Latin American cultures, that is cognate with the English name Frances.
-
C.
Manuela
Manuela is the given name of Maria Manuela, a 16th-century Portuguese princess who became Queen of Castile through marriage to King Philip II of Spain.
-
D.
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.
-
E.
María
"María" is a film featuring actress Taryn Power in a significant role.
- 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_69ad8b1db40081908b61ffa6b78afd4d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69adcbbe7a6081909b0f835a77941300 |
completed | March 8, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b4e50bfdb0819097bdfdd38f553ada |
completed | March 14, 2026, 4:33 a.m. |
Created at: March 8, 2026, 3:35 p.m.