Triple

T5828746
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 9 E129292 entity
Predicate station P726 FINISHED
Object Jasmin
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
 E548777 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: Jasmin | Statement: [Paris Métro Line 9, station, Jasmin]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jasmin
Context triple: [Paris Métro Line 9, station, Jasmin]
  • 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. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • C. 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.
  • 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. Kassia
    Kassia was a 9th-century Byzantine abbess, poet, and hymnographer renowned as one of the earliest and most important female composers in the Eastern Orthodox tradition.
  • 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: Jasmin
Triple: [Paris Métro Line 9, station, Jasmin]
Generated description
Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jasmin
Target entity description: Jasmin is a Paris Métro station in the 16th arrondissement, named after the 19th-century French poet Jasmin.
  • 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. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • C. 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.
  • 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. Kassia
    Kassia was a 9th-century Byzantine abbess, poet, and hymnographer renowned as one of the earliest and most important female composers in the Eastern Orthodox tradition.
  • 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_69c00849d55481908b4f9f5543e0bf6d completed March 22, 2026, 3:18 p.m.
NER Named-entity recognition batch_69c03467dfe48190b51757b33681bc20 completed March 22, 2026, 6:26 p.m.
NED1 Entity disambiguation (via context triple) batch_69c09863be3c8190bba357bf64e22917 completed March 23, 2026, 1:33 a.m.
NEDg Description generation batch_69c098d936d081909d930fc8b6b3fd67 completed March 23, 2026, 1:35 a.m.
NED2 Entity disambiguation (via description) batch_69c09947c5fc8190ba279ed0f991f9a9 completed March 23, 2026, 1:37 a.m.
Created at: March 22, 2026, 3:53 p.m.