Triple
T14779546
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Magic Carpet |
E347353
|
entity |
| Predicate | rescues |
P7320
|
FINISHED |
| Object | Jasmine |
E285209
|
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: [Magic Carpet, rescues, Jasmine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jasmine Context triple: [Magic Carpet, rescues, Jasmine]
-
A.
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.
-
B.
Jasmine
chosen
Jasmine is the independent and strong-willed princess of Agrabah from Disney's Aladdin, known for challenging tradition and seeking freedom beyond palace walls.
-
C.
Jasmine
Jasmine is a feminine given name commonly associated with the fragrant white flower and used in various cultures around the world.
-
D.
Jasmine
"Jasmine" is a critically acclaimed, genre-blending R&B/electronic track by British musician Jai Paul, known for its hazy production, distinctive vocal style, and cult status among music fans.
-
E.
Jasmin
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
- 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_69d822e9b9e08190bedcc31a163fda82 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69deca9c7cac8190ba900df95e42e318 |
completed | April 14, 2026, 11:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe0d00d46c8190b6289a9b8511a8fb |
completed | May 8, 2026, 4:19 p.m. |
Created at: April 10, 2026, 1:31 a.m.