Triple

T20634010
Position Surface form Disambiguated ID Type / Status
Subject DAT trucks E507029 entity
Predicate relatedTo P37 FINISHED
Object Nissan Motor Co., Ltd. NE NERFINISHED

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: Nissan Motor Co., Ltd. | Statement: [DAT trucks, relatedTo, Nissan Motor Co., Ltd.]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nissan Motor Co., Ltd.
Context triple: [DAT trucks, relatedTo, Nissan Motor Co., Ltd.]
  • A. Nissan chosen
    Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
  • B. Nissan
    Nissan is a river in southwestern Sweden that flows through the province of Halland before reaching the Kattegat.
  • C. Mitsubishi Motors
    Mitsubishi Motors is a Japanese automotive manufacturer known for producing a wide range of passenger cars, SUVs, and light commercial vehicles and for its involvement in global automotive alliances.
  • D. Toyota Motor Corporation
    Toyota Motor Corporation is a Japanese multinational automaker renowned for its reliable vehicles, pioneering of lean manufacturing and the Toyota Production System, and global leadership in hybrid technology.
  • E. Mitsubishi
    Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4bd4a0081908d4e97a590a33fb2 completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6ad0d808c81908a60abd02a22ed92 completed April 20, 2026, 10:47 p.m.
Created at: April 16, 2026, 11:42 a.m.