Triple

T13119672
Position Surface form Disambiguated ID Type / Status
Subject P-Wing E311688 entity
Predicate franchise P1500 FINISHED
Object Mario franchise E406903 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: Mario franchise | Statement: [P-Wing, franchise, Mario franchise]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mario franchise
Context triple: [P-Wing, franchise, Mario franchise]
  • A. Super Mario series chosen
    The Super Mario series is Nintendo’s flagship platform game franchise starring Mario in a wide variety of adventures across imaginative worlds.
  • B. Mario vs. Donkey Kong series
    The Mario vs. Donkey Kong series is a puzzle-platform video game franchise in which Mario navigates intricate, toy-themed levels to outwit Donkey Kong and solve object-based challenges.
  • C. Mario
    Mario is a fictional Italian plumber and the iconic protagonist of Nintendo's long-running Super Mario video game franchise.
  • D. Mario
    Mario is an American R&B singer, songwriter, and occasional actor best known for his early-2000s hits like "Let Me Love You."
  • E. Thor franchise
    The Thor franchise is a Marvel superhero film and media series centered on the Norse god of thunder, blending mythological elements with the Marvel Cinematic Universe.
  • 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_69d806a9fe888190b081e2d9ea665d6c completed April 9, 2026, 8:06 p.m.
NER Named-entity recognition batch_69d98196e69081909111407ee3d9f08e completed April 10, 2026, 11:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69f70a25c3b4819090511525babcd2ba completed May 3, 2026, 8:41 a.m.
Created at: April 9, 2026, 9:06 p.m.