Triple

T9308863
Position Surface form Disambiguated ID Type / Status
Subject Ops Consiva E223956 entity
Predicate nameElement P27866 FINISHED
Object Consiva
Consiva is a company or brand name, likely associated with operations or business services under the label "Ops Consiva."
 E792021 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: Consiva | Statement: [Ops Consiva, nameElement, Consiva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Consiva
Context triple: [Ops Consiva, nameElement, Consiva]
  • A. Sceptre
    Sceptre is a literary imprint known for publishing high-quality contemporary fiction and non-fiction, often with a focus on distinctive, award-winning voices.
  • B. Repossi
    Repossi is a high-end Italian jewelry house renowned for its avant-garde, architectural designs and strong heritage in fine jewelry craftsmanship.
  • C. Volax
    Volax is a distinctive traditional village on the Greek island of Tinos, known for its unique landscape of scattered granite boulders and its long-standing basket-weaving tradition.
  • D. Zyvex
    Zyvex is a pioneering nanotechnology company known for its early work in molecular nanotechnology and advanced manufacturing.
  • E. Ventris
    Ventris is the surname of Michael Ventris, the British architect and linguist renowned for deciphering the ancient script Linear B.
  • 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: Consiva
Triple: [Ops Consiva, nameElement, Consiva]
Generated description
Consiva is a company or brand name, likely associated with operations or business services under the label "Ops Consiva."
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Consiva
Target entity description: Consiva is a company or brand name, likely associated with operations or business services under the label "Ops Consiva."
  • A. Sceptre
    Sceptre is a literary imprint known for publishing high-quality contemporary fiction and non-fiction, often with a focus on distinctive, award-winning voices.
  • B. Repossi
    Repossi is a high-end Italian jewelry house renowned for its avant-garde, architectural designs and strong heritage in fine jewelry craftsmanship.
  • C. Volax
    Volax is a distinctive traditional village on the Greek island of Tinos, known for its unique landscape of scattered granite boulders and its long-standing basket-weaving tradition.
  • D. Zyvex
    Zyvex is a pioneering nanotechnology company known for its early work in molecular nanotechnology and advanced manufacturing.
  • E. Ventris
    Ventris is the surname of Michael Ventris, the British architect and linguist renowned for deciphering the ancient script Linear B.
  • 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_69ca8424d0f08190831e2e93c6533aeb completed March 30, 2026, 2:09 p.m.
NER Named-entity recognition batch_69cd1daba67c819081d53545d67ef127 completed April 1, 2026, 1:29 p.m.
NED1 Entity disambiguation (via context triple) batch_69d0c784f24481909af5f0daa1b8333a completed April 4, 2026, 8:10 a.m.
NEDg Description generation batch_69d0ca52acac8190aef4b5fa11594050 completed April 4, 2026, 8:22 a.m.
NED2 Entity disambiguation (via description) batch_69d0cc0968448190a89d8ea82d0c8102 completed April 4, 2026, 8:30 a.m.
Created at: March 30, 2026, 7:37 p.m.