Triple

T16069358
Position Surface form Disambiguated ID Type / Status
Subject King Kong Escapes E389817 entity
Predicate featuresCharacter P626 FINISHED
Object Mechani-Kong E389821 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: Mechani-Kong | Statement: [King Kong Escapes, featuresCharacter, Mechani-Kong]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mechani-Kong
Context triple: [King Kong Escapes, featuresCharacter, Mechani-Kong]
  • A. Mechani-Kong chosen
    Mechani-Kong is a robotic doppelgänger of King Kong featured as an antagonist in the King Kong franchise.
  • B. Swanky Kong
    Swanky Kong is a flashy, game-show–host-style member of the Kong family in the Donkey Kong video game series, known for running bonus game booths and mini-games.
  • C. Cranky Kong
    Cranky Kong is an elderly, grouchy member of the Kong family in the Donkey Kong series, often portrayed as a wise but sarcastic mentor figure.
  • D. Chunky Kong
    Chunky Kong is a large, strong but timid gorilla character from the Donkey Kong video game series, known for his immense strength and childlike personality.
  • E. Wrinkly Kong
    Wrinkly Kong is an elderly female Kong from the Donkey Kong video game series, often depicted as a wise, supportive character who assists players with advice and educational roles.
  • 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_69d86daf32ec8190a8c0466c8f49c3c0 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e183bb98c88190ae4b5773358078be completed April 17, 2026, 12:50 a.m.
NED1 Entity disambiguation (via context triple) batch_69ffe4827cd48190aa470c6537e72508 completed May 10, 2026, 1:50 a.m.
Created at: April 10, 2026, 4:57 a.m.