Triple

T32215660
Position Surface form Disambiguated ID Type / Status
Subject Formal language theory E822916 entity
Predicate hasKeyConcept P533 FINISHED
Object Myhill–Nerode theorem
The Myhill–Nerode theorem is a fundamental result in formal language theory that characterizes regular languages via an equivalence relation on strings and provides a method for constructing minimal deterministic finite automata.
 E1997105 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: Myhill–Nerode theorem | Statement: [Formal language theory, hasKeyConcept, Myhill–Nerode theorem]
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Myhill–Nerode theorem
Triple: [Formal language theory, hasKeyConcept, Myhill–Nerode theorem]
Generated description
The Myhill–Nerode theorem is a fundamental result in formal language theory that characterizes regular languages via an equivalence relation on strings and provides a method for constructing minimal deterministic finite automata.

Provenance (5 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_69f3490a3bec819097bc58d4731b9d08 completed April 30, 2026, 12:20 p.m.
NER Named-entity recognition batch_69f6bb96b9cc8190876f5f452d09f279 completed May 3, 2026, 3:05 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2f3b9e07148190b1c0485c4d8dca9c completed June 14, 2026, 11:39 p.m.
NEDg Description generation batch_6a2f3c207d988190835c5bda034bbc6c completed June 14, 2026, 11:41 p.m.
NED2 Entity disambiguation (via description) batch_6a2f3ef33fc08190bdedc81c93429535 completed June 14, 2026, 11:53 p.m.
Created at: May 1, 2026, 12:37 a.m.