Triple

T16996058
Position Surface form Disambiguated ID Type / Status
Subject Fuso E412317 entity
Predicate notableModel P1503 FINISHED
Object Rosa
Rosa is a notable model of the Mitsubishi Fuso line of commercial vehicles, recognized for its use in bus and light-duty transport applications.
E1245075 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: Rosa | Statement: [Fuso, notableModel, Rosa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Rosa
Context triple: [Fuso, notableModel, Rosa]
  • A. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • B. 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.
  • C. 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.
  • D. Rosa
    "Rosa" is a song by Belgian singer-songwriter Jacques Brel, known for its poetic lyrics and emotive, theatrical style characteristic of his chanson repertoire.
  • E. Rosa
    "Rosa" is a novella by Cynthia Ozick that follows a Holocaust survivor grappling with trauma, memory, and identity in postwar America.
  • 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: Rosa
Triple: [Fuso, notableModel, Rosa]
Generated description
Rosa is a notable model of the Mitsubishi Fuso line of commercial vehicles, recognized for its use in bus and light-duty transport applications.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Rosa
Target entity description: Rosa is a notable model of the Mitsubishi Fuso line of commercial vehicles, recognized for its use in bus and light-duty transport applications.
  • A. Rosa
    Rosa is a genus of flowering plants known for its ornamental roses, prized worldwide for their beauty, fragrance, and cultural symbolism.
  • B. 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.
  • C. Rosa
    Rosa is a feminine given name of Latin origin meaning "rose," used in many languages and cultures.
  • D. Rosa
    "Rosa" is a song by Belgian singer-songwriter Jacques Brel, known for its poetic lyrics and emotive, theatrical style characteristic of his chanson repertoire.
  • E. Rosa
    "Rosa" is a novella by Cynthia Ozick that follows a Holocaust survivor grappling with trauma, memory, and identity in postwar America.
  • 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_69d886cb581c8190ab05f4b429c9cd85 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d2879af081909665f9f838bcfbe7 completed April 18, 2026, 6:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc18e6988190b42b3d251cf00b98 completed May 10, 2026, 7:27 p.m.
NEDg Description generation batch_6a0114d5aeb0819086f1a5d279ac0d0f completed May 10, 2026, 11:29 p.m.
NED2 Entity disambiguation (via description) batch_6a0115c967b0819088e2335fd45d755b completed May 10, 2026, 11:33 p.m.
Created at: April 10, 2026, 5:32 a.m.