Triple

T19087198
Position Surface form Disambiguated ID Type / Status
Subject Zastava 102 E467181 entity
Predicate manufacturer P490 FINISHED
Object Zastava Automobiles 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: Zastava Automobiles | Statement: [Zastava 102, manufacturer, Zastava Automobiles]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Zastava Automobiles
Context triple: [Zastava 102, manufacturer, Zastava Automobiles]
  • A. Zastava Automobiles chosen
    Zastava Automobiles is a Serbian car manufacturer best known internationally for producing the Yugo and other affordable compact vehicles during the Yugoslav era.
  • B. Tatra automobile factory
    The Tatra automobile factory is a historic Czech manufacturer renowned for its innovative, often air-cooled vehicles and heavy trucks, and is one of the oldest car makers in the world.
  • C. Adria Mobil
    Adria Mobil is a Slovenian professional cycling team known for developing top riders who often progress to the WorldTour level.
  • D. Steyr-Daimler-Puch
    Steyr-Daimler-Puch was a major Austrian industrial conglomerate best known for producing firearms, vehicles, and machinery throughout the 20th century.
  • E. GAZ Group
    GAZ Group is a major Russian automotive manufacturer best known for producing commercial vehicles, trucks, and buses.
  • 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_69d8dd05ac4c8190b1967d8f97f3fb2f completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e348c980819096667ee6e7a7f36f completed April 20, 2026, 8:26 a.m.
Created at: April 10, 2026, 12:04 p.m.