Triple
T18330656
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabella Dylan Bugliari |
E439129
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Elizabella |
—
|
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: Elizabella | Statement: [Elizabella Dylan Bugliari, givenName, Elizabella]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elizabella Context triple: [Elizabella Dylan Bugliari, givenName, Elizabella]
-
A.
Elisabeth
chosen
Elisabeth is a feminine given name of Hebrew origin, commonly used in various European languages as a form of Elizabeth.
-
B.
Elisabeth
Elisabeth is a metro station on the Brussels Metro system in Brussels, Belgium.
-
C.
Isabella
Isabella was a Portuguese noblewoman of the House of Braganza who held the title of Duchess of Guimarães in the 16th century.
-
D.
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.
-
E.
Isabella
Isabella was a 15th-century Portuguese infanta who became Queen of Castile through her marriage to King John II.
- 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_69d8b916a2d081909e249e4902f6aad9 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e50ec900808190bc4468270e0957c1 |
completed | April 19, 2026, 5:20 p.m. |
Created at: April 10, 2026, 10:36 a.m.