Triple
T10004499
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jasmine Tookes |
E198208
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Jasmine |
E583019
|
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: Jasmine | Statement: [Jasmine Tookes, givenName, Jasmine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jasmine Context triple: [Jasmine Tookes, givenName, Jasmine]
-
A.
Jasmine
Jasmine is the independent and strong-willed princess of Agrabah from Disney's Aladdin, known for challenging tradition and seeking freedom beyond palace walls.
-
B.
Jasmine
Jasmine is a popular behavior-driven development (BDD) testing framework for JavaScript, commonly used for unit testing in both browser and Node.js environments.
-
C.
Jasmine
chosen
Jasmine is a feminine given name commonly associated with the fragrant white flower and used in various cultures around the world.
-
D.
Jasmin
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
-
E.
Bunga
Bunga is a brave and energetic honey badger who serves as the comedic yet fearless member of the Lion Guard in the Disney Junior series.
- 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_69ca830fcca48190bbbd9b20c233835f |
completed | March 30, 2026, 2:05 p.m. |
| NER | Named-entity recognition | batch_69cdcd141ec08190b857fe7e15a8df93 |
completed | April 2, 2026, 1:57 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26a5058a08190b850d30ca0ee8bc8 |
completed | April 5, 2026, 1:57 p.m. |
Created at: March 30, 2026, 8:51 p.m.