Triple

T17280915
Position Surface form Disambiguated ID Type / Status
Subject Toyota Aygo E419523 entity
Predicate relatedModel P37 FINISHED
Object Peugeot 108 E1261830 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: Peugeot 108 | Statement: [Toyota Aygo, relatedModel, Peugeot 108]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Peugeot 108
Context triple: [Toyota Aygo, relatedModel, Peugeot 108]
  • A. Peugeot 108 chosen
    The Peugeot 108 is a compact city car produced by the French automaker Peugeot, known for its small size, urban-friendly design, and efficient engines.
  • B. Peugeot 107
    The Peugeot 107 is a compact city car produced by the French manufacturer Peugeot, known for its small size, fuel efficiency, and urban-friendly design.
  • C. Peugeot 208
    The Peugeot 208 is a popular supermini hatchback produced by the French automaker Peugeot, known for its stylish design, efficient engines, and modern technology features.
  • D. Peugeot 106
    The Peugeot 106 is a small city car produced by the French manufacturer Peugeot in the 1990s and early 2000s, known for its compact size, affordability, and popularity in European markets.
  • E. Peugeot 301
    The Peugeot 301 is a compact sedan designed primarily for emerging markets, known for its affordability, practicality, and robust build for varied road conditions.
  • 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_69d886da626481908a14ce7830329a35 completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e43329904c8190a4cbc856b9b94ff8 completed April 19, 2026, 1:43 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195488d2c81908ac6c19f54f61796 completed May 11, 2026, 8:37 a.m.
Created at: April 10, 2026, 5:40 a.m.