Triple

T26991891
Position Surface form Disambiguated ID Type / Status
Subject ConcreteState E679879 entity
Predicate participatesIn P149 FINISHED
Object State design pattern
The State design pattern is a behavioral software design pattern that lets an object alter its behavior when its internal state changes by encapsulating state-specific logic into separate state classes.
 E1750885 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: State design pattern | Statement: [ConcreteState, participatesIn, State design pattern]
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: State design pattern
Triple: [ConcreteState, participatesIn, State design pattern]
Generated description
The State design pattern is a behavioral software design pattern that lets an object alter its behavior when its internal state changes by encapsulating state-specific logic into separate state classes.

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_69eeeb5138ac8190b3c273ddc659a54f completed April 27, 2026, 4:51 a.m.
NER Named-entity recognition batch_69f621915ee48190b508025afa2fb4fc completed May 2, 2026, 4:08 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1229b82ae48190b736a900f782af54 completed May 23, 2026, 10:27 p.m.
NEDg Description generation batch_6a122b0175188190be2cb1b106694112 completed May 23, 2026, 10:32 p.m.
NED2 Entity disambiguation (via description) batch_6a122b8f21ec81908aaaf7e829c62f85 completed May 23, 2026, 10:34 p.m.
Created at: April 27, 2026, 6:52 a.m.