Triple

T2160485
Position Surface form Disambiguated ID Type / Status
Subject Isuzu E47989 entity
Predicate hasModel P2390 FINISHED
Object Isuzu KB E240271 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: Isuzu KB | Statement: [Isuzu, hasModel, Isuzu KB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Isuzu KB
Context triple: [Isuzu, hasModel, Isuzu KB]
  • A. Isuzu Panther
    The Isuzu Panther is a multi-purpose vehicle (MPV) produced by Isuzu, popular in Southeast Asia for its durability, utility, and use as a family and commercial transport.
  • B. Isuzu
    Isuzu is a Japanese automotive manufacturer best known for producing commercial vehicles, pickup trucks, and diesel engines for global markets.
  • C. Isuzu Elf
    The Isuzu Elf is a line of light-duty commercial trucks produced by Japanese manufacturer Isuzu, widely used for urban delivery and utility applications.
  • D. Isuzu D-Max chosen
    The Isuzu D-Max is a popular mid-size pickup truck known for its durability, strong diesel engines, and widespread use in both commercial and personal applications worldwide.
  • E. Hino
    Hino is a town in Shiga Prefecture, Japan, known for its historical streetscapes and traditional industries.
  • 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_69a88a1d1fd8819088b34990d69a712f completed March 4, 2026, 7:38 p.m.
NER Named-entity recognition batch_69abbe8894d481908eda9363fd36fea6 completed March 7, 2026, 5:58 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae7ef6b5988190a50e7c807756347e completed March 9, 2026, 8:04 a.m.
Created at: March 4, 2026, 7:45 p.m.