Triple

T24600927

Position	Surface form	Disambiguated ID	Type / Status
Subject	Paul trap	`E608818`	entity
Predicate	stabilityDescribedBy	`P100381`	FINISHED
Object	Mathieu equations The Mathieu equations are a class of linear differential equations with periodic coefficients that describe the stability and behavior of systems under parametric excitation, such as particles confined in a Paul trap.	`E1640652`	NE FINISHED

How this triple was built (3 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: Mathieu equations | Statement: [Paul trap, stabilityDescribedBy, Mathieu equations]

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: Mathieu equations
Triple: [Paul trap, stabilityDescribedBy, Mathieu equations]

Generated description

The Mathieu equations are a class of linear differential equations with periodic coefficients that describe the stability and behavior of systems under parametric excitation, such as particles confined in a Paul trap.

PD Predicate disambiguation gpt-5-mini-2025-08-07

Target predicate: stabilityDescribedBy
Context triple: [Paul trap, stabilityDescribedBy, Mathieu equations]

A. stabilityDescription
Indicates how stable, consistent, or enduring the relationship, condition, or state between the entities is over time.
B. stabilityCharacteristic
Indicates that one entity specifies a property or feature related to the stability or steadiness of another entity or system.
C. stabilityDefinition
Indicates the formal explanation or criteria that define what is meant by stability in a given context or system.
D. stabilizedBy
Indicates that an entity’s state, structure, or behavior is made more steady, secure, or resistant to change through the influence or support of another entity.
E. haveStabilityDeterminedBy chosen
Indicates that the stability of one entity is determined or governed by another specified factor or entity.
F. None of above.

Provenance (6 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_69e2c4d060e08190ac9f7c49b1036e20`	completed	April 17, 2026, 11:40 p.m.
NER	Named-entity recognition	`batch_69f2be044d4c819094e14eda28d371a7`	completed	April 30, 2026, 2:27 a.m.
NED1	Entity disambiguation (via context triple)	`batch_6a0ff88a53b48190b31544940ac1a633`	completed	May 22, 2026, 6:32 a.m.
NEDg	Description generation	`batch_6a0ff9cdbbe08190b9c04acc258a32e4`	completed	May 22, 2026, 6:38 a.m.
NED2	Entity disambiguation (via description)	`batch_6a0ffa70e32c81909345bb45de585d83`	completed	May 22, 2026, 6:40 a.m.
PD	Predicate disambiguation	`batch_69f2a6ca751c8190a040c10d701ecf3a`	completed	April 30, 2026, 12:48 a.m.

Created at: April 18, 2026, 2:30 a.m.