Triple

T8603018
Position Surface form Disambiguated ID Type / Status
Subject TBWA Worldwide E203723 entity
Predicate notableClient P7186 FINISHED
Object Nissan E21556 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: Nissan | Statement: [TBWA Worldwide, notableClient, Nissan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nissan
Context triple: [TBWA Worldwide, notableClient, Nissan]
  • A. Nissan
    Nissan is a river in southwestern Sweden that flows through the province of Halland before reaching the Kattegat.
  • B. Nissan chosen
    Nissan is a major Japanese automobile manufacturer known for producing a wide range of passenger cars, trucks, and electric vehicles sold globally.
  • 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. Mitsubishi
    Mitsubishi is a major Japanese multinational conglomerate known for its diverse businesses in industries such as automotive, heavy industry, finance, and electronics.
  • E. Datsun
    Datsun is a historic Japanese automobile brand, revived as a budget-focused marque under the Renault–Nissan–Mitsubishi Alliance and known for its small, affordable cars.
  • 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_69ca832b56948190ba751cec255308f1 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cc46dc23448190a5eb63455578427e completed March 31, 2026, 10:12 p.m.
NED1 Entity disambiguation (via context triple) batch_69cea8f8dfa4819080c8ed475a84be41 completed April 2, 2026, 5:35 p.m.
Created at: March 30, 2026, 6:24 p.m.