Triple
T9812406
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella of Parma |
E238305
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Isabella |
E387907
|
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: Isabella | Statement: [Isabella of Parma, givenName, Isabella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabella Context triple: [Isabella of Parma, givenName, Isabella]
-
A.
Isabella
Isabella is a virtuous and resourceful young noblewoman in Horace Walpole’s Gothic novel "The Castle of Otranto," whose peril and resistance drive much of the story’s suspense and drama.
-
B.
Isabella
Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
-
C.
Isabella
chosen
Isabella is the given name of Mrs Beeton, the famed 19th-century English author of the influential household management guide "Mrs Beeton's Book of Household Management."
-
D.
Isabella
Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
-
E.
Isabella
Isabella is a devout and principled novice nun in Shakespeare's play "Measure for Measure," whose moral integrity is tested by corrupt authority.
- 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_69ca84defac48190abc1148804f184c1 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb222ba788190a9085272a3de7852 |
completed | April 2, 2026, 12:02 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1e41725948190a40ddcf9552e9bf1 |
completed | April 5, 2026, 4:24 a.m. |
Created at: March 30, 2026, 8:30 p.m.