Triple

T3998569
Position Surface form Disambiguated ID Type / Status
Subject Tom Sawyer universe E87156 entity
Predicate featuresCharacter P626 FINISHED
Object Muff Potter E62966 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: Muff Potter | Statement: [Tom Sawyer universe, featuresCharacter, Muff Potter]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Muff Potter
Context triple: [Tom Sawyer universe, featuresCharacter, Muff Potter]
  • A. Muff Potter chosen
    Muff Potter is a hapless, kind-hearted but often drunk vagrant and accused murderer in Mark Twain’s novel "The Adventures of Tom Sawyer."
  • B. Louis de Potter
    Louis de Potter was a Belgian liberal journalist and politician who played a leading role in the movement that sparked the Belgian Revolution and the country’s independence in 1830.
  • C. Puckman
    Puckman is the ice hockey–themed mascot representing Rensselaer Polytechnic Institute’s athletic teams.
  • D. Mr. Plod
    Mr. Plod is the bumbling village policeman character from Enid Blyton’s Noddy stories, known for trying to keep order in Toyland.
  • E. The Mister
    The Mister is a contemporary romance novel by E. L. James, known for its Cinderella-style love story and for being her follow-up to the Fifty Shades series.
  • 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa4065ac8190a898a1025365b8e9 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b54c57f60c819080e237b4b056d73b completed March 14, 2026, 11:54 a.m.
Created at: March 9, 2026, 3:34 p.m.