Triple
T22931720
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Isabella |
E569457
|
entity |
| Predicate | variantOf |
P4680
|
FINISHED |
| Object | Isabelle |
—
|
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: Isabelle | Statement: [Isabella, variantOf, Isabelle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Isabelle Context triple: [Isabella, variantOf, Isabelle]
-
A.
Isabelle
Isabelle is a popular character from the Animal Crossing series who also appears as a playable racer in Mario Kart 8.
-
B.
Isabelle
Isabelle is a prominent interactive theorem prover and proof assistant widely used in formal verification and mathematical logic research.
-
C.
Isabelle
chosen
Isabelle is a feminine given name of French origin, commonly used in many countries and cultures.
-
D.
Isabelle
Isabelle is a central female character in Théophile Gautier's novel "Le Capitaine Fracasse," known for her beauty, virtue, and role as the love interest of the protagonist.
-
E.
Isabelle
Isabelle is a central character in the 2010 science fiction action film "Predators," portrayed as one of the elite human warriors stranded on an alien game preserve.
- 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_69e2458f7d008190901dccbaebeaba24 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f1813260608190bb9e1ca704c7e12f |
completed | April 29, 2026, 3:55 a.m. |
Created at: April 17, 2026, 3:44 p.m.