Triple

T22763712
Position Surface form Disambiguated ID Type / Status
Subject Ziff-Davis Publishing Company E563061 entity
Predicate notableWork P4 FINISHED
Object Car and Driver 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: Car and Driver | Statement: [Ziff-Davis Publishing Company, notableWork, Car and Driver]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Car and Driver
Context triple: [Ziff-Davis Publishing Company, notableWork, Car and Driver]
  • A. Car and Driver chosen
    Car and Driver is a leading American automotive enthusiast magazine and media brand known for its in-depth car reviews, comparison tests, and industry news.
  • B. Autocar
    Autocar is a long-running British automotive magazine known for its car reviews, industry news, and road tests.
  • C. What Car? magazine
    What Car? magazine is a long-running British automotive publication known for its in-depth car reviews, consumer advice, and influential annual awards.
  • D. Road & Track
    Road & Track is an American automotive enthusiast magazine known for its in-depth coverage of performance cars, motorsports, and automotive culture.
  • E. Z Cars
    Z Cars is a pioneering British television police drama series that aired in the 1960s and 1970s, known for its gritty, realistic portrayal of everyday policing in the fictional town of Newtown.
  • 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_69e24552e11c81909c2d61578a558bd7 completed April 17, 2026, 2:36 p.m.
NER Named-entity recognition batch_69f17a7dd9348190ba7c74362ad30b1e completed April 29, 2026, 3:26 a.m.
Created at: April 17, 2026, 3:26 p.m.