Triple

T5922317
Position Surface form Disambiguated ID Type / Status
Subject Should.js E131727 entity
Predicate compatibleWith P203 FINISHED
Object 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.
 E559314 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: [Should.js, compatibleWith, Jasmine]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jasmine
Context triple: [Should.js, compatibleWith, 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. Jasmin
    Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
  • C. Sakura
    Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
  • D. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • E. Rosa
    Rosa is the birth name of Linda Christian, a Mexican film actress known as the first "Bond girl" for her role in the 1954 television adaptation of Casino Royale.
  • 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: [Should.js, compatibleWith, Jasmine]
Generated description
Jasmine is a popular behavior-driven development (BDD) testing framework for JavaScript, commonly used for unit testing in both browser and Node.js environments.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jasmine
Target entity description: Jasmine is a popular behavior-driven development (BDD) testing framework for JavaScript, commonly used for unit testing in both browser and Node.js environments.
  • 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. Jasmin
    Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
  • C. Sakura
    Sakura is a Japanese high-speed Shinkansen train service that operates mainly on the Sanyo and Kyushu Shinkansen lines.
  • D. Rosa
    Rosa is a celebrated poem by Nikki Giovanni that honors civil rights icon Rosa Parks and reflects on the broader struggle for racial justice.
  • 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_69c0085a1ed08190a7e9a8b6323fd680 completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c03804d9808190829a418adb7864aa completed March 22, 2026, 6:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69c0e39ea48c8190a968f81f0e26ccbf completed March 23, 2026, 6:54 a.m.
NEDg Description generation batch_69c0f602b79881909a6d971972f760b1 completed March 23, 2026, 8:12 a.m.
NED2 Entity disambiguation (via description) batch_69c0f6a75b908190b35d13b9593cf21f completed March 23, 2026, 8:15 a.m.
Created at: March 22, 2026, 4 p.m.