Triple

T8754688
Position Surface form Disambiguated ID Type / Status
Subject GAZ Group E208044 entity
Predicate brand P1500 FINISHED
Object Vector
Vector is a commercial vehicle brand produced by the Russian automotive manufacturer GAZ Group, known primarily for its buses.
 E754548 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: Vector | Statement: [GAZ Group, brand, Vector]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Vector
Context triple: [GAZ Group, brand, Vector]
  • A. Vector
    Vector is a mid-range, sport-oriented trim level of the Saab 9-3 that typically offers enhanced performance and upgraded interior and exterior features compared to base models.
  • B. Vector
    Vector is a villainous character from the Despicable Me franchise, known for his orange tracksuit, bowl haircut, and high-tech gadgets.
  • C. Vectors
    "Vectors" is a science fiction work by American author Michael Kube-McDowell, known for its exploration of complex futuristic and technological themes.
  • D. vec
    vec is the ISO 639-3 code for the Venetian language, a Romance language spoken primarily in the Veneto region of Italy and surrounding areas.
  • E. VEC
    VEC is the vehicle registration code used on license plates for vehicles registered in the District of Vechta in Lower Saxony, Germany.
  • 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: Vector
Triple: [GAZ Group, brand, Vector]
Generated description
Vector is a commercial vehicle brand produced by the Russian automotive manufacturer GAZ Group, known primarily for its buses.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Vector
Target entity description: Vector is a commercial vehicle brand produced by the Russian automotive manufacturer GAZ Group, known primarily for its buses.
  • A. Vector
    Vector is a mid-range, sport-oriented trim level of the Saab 9-3 that typically offers enhanced performance and upgraded interior and exterior features compared to base models.
  • B. Vector
    Vector is a villainous character from the Despicable Me franchise, known for his orange tracksuit, bowl haircut, and high-tech gadgets.
  • C. Vectors
    "Vectors" is a science fiction work by American author Michael Kube-McDowell, known for its exploration of complex futuristic and technological themes.
  • D. vec
    vec is the ISO 639-3 code for the Venetian language, a Romance language spoken primarily in the Veneto region of Italy and surrounding areas.
  • E. VEC
    VEC is the vehicle registration code used on license plates for vehicles registered in the District of Vechta in Lower Saxony, Germany.
  • 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_69ca835cd6b08190bd7c63db92f53c86 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cc5dd83088819082cf54adc0c04243 completed March 31, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69cf43305664819085e762e42b138754 completed April 3, 2026, 4:33 a.m.
NEDg Description generation batch_69cf452b237c8190958f7b42e9611e7b completed April 3, 2026, 4:42 a.m.
NED2 Entity disambiguation (via description) batch_69cf45e6f4108190ac6955264b466abb completed April 3, 2026, 4:45 a.m.
Created at: March 30, 2026, 6:39 p.m.