Triple

T1903656
Position Surface form Disambiguated ID Type / Status
Subject Scania E37748 entity
Predicate predecessor P97 FINISHED
Object Vabis
Vabis was a Swedish automotive and engineering company that later merged to form Scania-Vabis, a predecessor of the modern Scania AB.
 E212626 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: Vabis | Statement: [Scania, predecessor, Vabis]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vabis
Context triple: [Scania, predecessor, Vabis]
  • A. Bacavi
    Bacavi is a traditional Hopi village located on the Hopi Reservation in northeastern Arizona.
  • B. Varaha
    Varaha is the boar incarnation of the Hindu god Vishnu, revered for rescuing the earth from cosmic waters and defeating the demon Hiranyaksha.
  • C. Vivarais
    Vivarais is a historical region in south-central France, known for its rugged landscapes, part of the broader Massif Central, and its traditional rural culture.
  • D. Vangunu
    Vangunu is an Oceanic language of the Meso-Melanesian group spoken on Vangunu Island in the Solomon Islands.
  • E. Nobsa
    Nobsa is a Colombian town known for its traditional wool textiles and crafts, located in the Boyacá Department.
  • 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: Vabis
Triple: [Scania, predecessor, Vabis]
Generated description
Vabis was a Swedish automotive and engineering company that later merged to form Scania-Vabis, a predecessor of the modern Scania AB.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vabis
Target entity description: Vabis was a Swedish automotive and engineering company that later merged to form Scania-Vabis, a predecessor of the modern Scania AB.
  • A. Bacavi
    Bacavi is a traditional Hopi village located on the Hopi Reservation in northeastern Arizona.
  • B. Varaha
    Varaha is the boar incarnation of the Hindu god Vishnu, revered for rescuing the earth from cosmic waters and defeating the demon Hiranyaksha.
  • C. Vivarais
    Vivarais is a historical region in south-central France, known for its rugged landscapes, part of the broader Massif Central, and its traditional rural culture.
  • D. Vangunu
    Vangunu is an Oceanic language of the Meso-Melanesian group spoken on Vangunu Island in the Solomon Islands.
  • E. Nobsa
    Nobsa is a Colombian town known for its traditional wool textiles and crafts, located in the Boyacá Department.
  • 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_69a8861be7148190a680937ec451a304 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69abb1909aec8190b3259c8f969ce81e completed March 7, 2026, 5:03 a.m.
NED1 Entity disambiguation (via context triple) batch_69adeaf768888190885ffa1632537445 completed March 8, 2026, 9:32 p.m.
NEDg Description generation batch_69adeb8b3d2c8190b13c03ce944f436a completed March 8, 2026, 9:35 p.m.
NED2 Entity disambiguation (via description) batch_69adec123cc481908e55dfbe4f4da095 completed March 8, 2026, 9:37 p.m.
Created at: March 4, 2026, 7:35 p.m.