Triple
T8527321
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella of England |
E201849
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Isabella |
E340044
|
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 England, givenName, Isabella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabella Context triple: [Isabella of England, givenName, Isabella]
-
A.
Isabella
Isabella was a Spanish Habsburg archduchess who governed the Spanish Netherlands in the late 16th and early 17th centuries.
-
B.
Isabella
Isabella was a 15th-century Aragonese princess who became Queen of Portugal through her marriage to King Manuel I.
-
C.
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.
-
D.
Isabella
chosen
Isabella was an English princess of the 13th century, daughter of King John of England, who became Lady de Coucy through marriage into the French nobility.
-
E.
Isabella
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."
- 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_69ca83228b24819085d22e7dc99f5d94 |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cbe6477100819081fa20cb6b8ea3d7 |
completed | March 31, 2026, 3:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ce6d54ef908190970a1010c8018abd |
completed | April 2, 2026, 1:21 p.m. |
Created at: March 30, 2026, 6:16 p.m.