Triple
T20220349
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella of Aragon |
E495235
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Isabella |
—
|
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: Isabella | Statement: [Isabella of Aragon, givenName, Isabella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabella Context triple: [Isabella of Aragon, givenName, Isabella]
-
A.
Isabella
chosen
Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
-
B.
Isabella
Isabella was a Portuguese noblewoman of the House of Braganza who held the title of Duchess of Guimarães in the 16th century.
-
C.
Isabella
Isabella is a Danish princess, the eldest daughter of Crown Prince Frederik and Crown Princess Mary and a member of the Danish royal family.
-
D.
Isabella
Isabella is a fictional character portrayed by American actress Lexi Underwood.
-
E.
Isabella
Isabella was a powerful late 15th-century queen of Castile who, alongside her husband Ferdinand II of Aragon, completed the Reconquista and sponsored Christopher Columbus’s voyage that led to the European discovery of the Americas.
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66edc88148190b003b72eb4c69da5 |
completed | April 20, 2026, 6:22 p.m. |
Created at: April 11, 2026, 11:39 p.m.