Triple

T2945728
Position Surface form Disambiguated ID Type / Status
Subject Super Mario World E79495 entity
Predicate featuresCharacter P626 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 World, featuresCharacter, Luigi]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Luigi
Context triple: [Super Mario World, featuresCharacter, Luigi]
  • A. Luigi
    Luigi is a small, enthusiastic Italian Fiat 500 who runs a tire shop and provides comic relief in Pixar's Cars franchise.
  • B. 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.
  • C. 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.
  • 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_69ad8b1089588190b74d9e2505e45762 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69ad98b2752481908ec6f9a9cc24c0a7 completed March 8, 2026, 3:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69b0fc7584588190a8f67621406636ed completed March 11, 2026, 5:24 a.m.
Created at: March 8, 2026, 2:56 p.m.