Triple

T2934682
Position Surface form Disambiguated ID Type / Status
Subject Super Mario Bros. 3 E79236 entity
Predicate character P662 FINISHED
Object Luigi E11428 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: Luigi | Statement: [Super Mario Bros. 3, character, Luigi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luigi
Context triple: [Super Mario Bros. 3, character, Luigi]
  • A. Luigi
    Luigi is the birth name of Hall of Fame basketball coach Geno Auriemma, renowned for leading the University of Connecticut women's team to multiple national championships.
  • B. Luigi
    Luigi is a small, enthusiastic Italian Fiat 500 who runs a tire shop and provides comic relief in Pixar's Cars franchise.
  • C. Luigi chosen
    Luigi is a timid yet heroic green-clad plumber from Nintendo’s Mario franchise, known as Mario’s younger brother and frequent co-adventurer.
  • D. Wario
    Wario is a greedy, mischievous antihero in Nintendo’s Mario franchise, known for his brutish strength, distinctive yellow and purple outfit, and starring role in the Wario Land and WarioWare game series.
  • E. Mario Pani
    Mario Pani was a prominent 20th-century Mexican architect and urban planner known for his influential modernist housing complexes and large-scale urban projects in Mexico City.
  • 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_69ad8b0fbab081908f6a61567c045d8d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad983c84688190aa7ed5b8091fb140 completed March 8, 2026, 3:39 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0867ba1b48190a54d00c32b075548 completed March 10, 2026, 9 p.m.
Created at: March 8, 2026, 2:56 p.m.