Triple

T2032619
Position Surface form Disambiguated ID Type / Status
Subject Stelios Haji-Ioannou E44551 entity
Predicate founderOf P104 FINISHED
Object easyCar E223781 NE FINISHED

How this triple was built (2 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: easyCar | Statement: [Stelios Haji-Ioannou, founderOf, easyCar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: easyCar
Context triple: [Stelios Haji-Ioannou, founderOf, easyCar]
  • A. easyCar chosen
    easyCar is a car rental company within the easyGroup family of low-cost travel and service brands.
  • B. Lancia Ypsilon
    The Lancia Ypsilon is a small Italian city car known for its stylish design, upscale interior, and long-running popularity in the European supermini segment.
  • C. Lotus Elise
    The Lotus Elise is a lightweight, mid-engined British sports car renowned for its agile handling and minimalist, driver-focused design.
  • D. Maxus
    Maxus is a commercial vehicle brand known for producing vans, pickups, and light trucks, owned by the Chinese automotive giant SAIC Motor.
  • E. Peugeot Expert
    The Peugeot Expert is a light commercial van produced by the French automaker Peugeot, commonly used for cargo and passenger transport in European markets.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69a889144f2481909932f0746a93023d completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb93255248190bd47a54a7b3c7447 completed March 7, 2026, 5:35 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1fed963c8190ac205b46f93ad650 completed March 9, 2026, 1:18 a.m.
Created at: March 4, 2026, 7:39 p.m.