Triple

T1513400
Position Surface form Disambiguated ID Type / Status
Subject Mercedes Barcha E32064 entity
Predicate givenName P17 FINISHED
Object Mercedes E69074 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: Mercedes | Statement: [Mercedes Barcha, givenName, Mercedes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mercedes
Context triple: [Mercedes Barcha, givenName, Mercedes]
  • A. Mercedes chosen
    Mercedes is a courageous and compassionate housekeeper who secretly aids the Spanish Maquis resistance in Guillermo del Toro’s dark fantasy film "Pan’s Labyrinth."
  • B. Mercedes-Benz
    Mercedes-Benz is a German luxury automobile manufacturer renowned for its premium cars, engineering innovation, and iconic three-pointed star logo.
  • C. Porsche
    Porsche is a German luxury automobile manufacturer renowned for its high-performance sports cars, SUVs, and engineering excellence.
  • D. Mercedes-Benz USA
    Mercedes-Benz USA is the American subsidiary of the German luxury automobile manufacturer Mercedes-Benz, responsible for marketing, sales, and distribution of its vehicles in the United States.
  • E. Ferrari
    Ferrari is an Italian luxury sports car manufacturer renowned for its high-performance vehicles, racing heritage, and iconic prancing horse emblem.
  • 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_69a885e8caf88190a5fbb6159ce87786 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69a907d901ac8190be55ed4bac609d1d completed March 5, 2026, 4:34 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad23409fd481909834aaf0dc4641f6 completed March 8, 2026, 7:20 a.m.
Created at: March 4, 2026, 7:26 p.m.