Triple
T6279090
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jasmine Trias |
E140736
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object |
Jasmine
Jasmine is a feminine given name commonly associated with the fragrant white flower and used in various cultures around the world.
|
E583019
|
NE FINISHED |
How this triple was built (4 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 Trias, givenName, Jasmine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jasmine Context triple: [Jasmine Trias, 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.
Jasmin
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
-
D.
Sakura
Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
-
E.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jasmine Triple: [Jasmine Trias, givenName, Jasmine]
Generated description
Jasmine is a feminine given name commonly associated with the fragrant white flower and used in various cultures around the world.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jasmine Target entity description: Jasmine is a feminine given name commonly associated with the fragrant white flower and used in various cultures around the world.
-
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.
Jasmin
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
-
D.
Sakura
Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
-
E.
Rosa
Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
- F. None of above. chosen
Provenance (5 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_69c008cc158881908df6ec94a911c736 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c063dc55d48190b5ed48a50f3a742e |
completed | March 22, 2026, 9:49 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c519549cf0819096d01c23f6c915eb |
completed | March 26, 2026, 11:32 a.m. |
| NEDg | Description generation | batch_69c51dfe50c4819084c43ca4d6cced35 |
completed | March 26, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69c58d97bf808190a2f8f101cf46a16b |
completed | March 26, 2026, 7:48 p.m. |
Created at: March 22, 2026, 4:26 p.m.