Triple

T6370944
Position Surface form Disambiguated ID Type / Status
Subject Curry encoding E143341 entity
Predicate contrastsWith P278 FINISHED
Object Church encoding E588094 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: Church encoding | Statement: [Curry encoding, contrastsWith, Church encoding]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Church encoding
Context triple: [Curry encoding, contrastsWith, Church encoding]
  • A. Church encoding chosen
    Church encoding is a means of representing data and operators in the lambda calculus using only functions, forming the theoretical basis for functional programming representations of numbers, booleans, and data structures.
  • B. Scott encoding
    Scott encoding is a method in lambda calculus for representing algebraic data types and their pattern matching behavior using higher-order functions.
  • C. Gödel numbering
    Gödel numbering is a method in mathematical logic that encodes symbols, formulas, and proofs as unique natural numbers, enabling arithmetic to represent and reason about syntactic statements.
  • D. Curry encoding
    Curry encoding is a technique in lambda calculus for representing data structures and algebraic types purely as higher-order functions.
  • E. Baconian method
    The Baconian method is a systematic approach to scientific inquiry that emphasizes empirical observation, experimentation, and inductive reasoning to derive general principles from particular facts.
  • 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_69c008d8c61081908bcaf61510d881ed completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c068289eac8190a17affed87340c1f completed March 22, 2026, 10:07 p.m.
NED1 Entity disambiguation (via context triple) batch_69c6386f361c819098dbe01b0cb07b06 completed March 27, 2026, 7:57 a.m.
Created at: March 22, 2026, 4:33 p.m.