Triple

T27799163

Position	Surface form	Disambiguated ID	Type / Status
Subject	Kafka Streams	`E702192`	entity
Predicate	persistsStateTo	`P31904`	FINISHED
Object	Kafka changelog topics Kafka changelog topics are internal Kafka topics used to durably log and replicate state changes for stateful stream processing applications.	`E702192`	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: Kafka changelog topics | Statement: [Kafka Streams, persistsStateTo, Kafka changelog topics]

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: Kafka changelog topics
Triple: [Kafka Streams, persistsStateTo, Kafka changelog topics]

Generated description

Kafka changelog topics are internal Kafka topics used to durably log and replicate state changes for stateful stream processing applications.

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

Target predicate: persistsStateTo
Context triple: [Kafka Streams, persistsStateTo, Kafka changelog topics]

A. persistsAfter
Indicates that one state, condition, or effect continues to exist after a specified event, time point, or other state has occurred or ended.
B. persistsAcross
Indicates that a state, condition, or relationship continues to hold unchanged across different times, situations, or contexts.
C. allowsPersistentStateInProcess
Indicates that an entity permits maintaining persistent state within an ongoing process or execution context.
D. supportsPersistence chosen
Indicates that one entity enables or provides the capability for another entity’s data or state to be stored and retained over time.
E. persistence
Indicates a continued or repeated existence, occurrence, or effort of something over time despite potential changes or obstacles.
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_69ef8408e0588190977cffa32dc33a29`	completed	April 27, 2026, 3:43 p.m.
NER	Named-entity recognition	`batch_69f64dbbaefc8190952b8320bf4397d8`	completed	May 2, 2026, 7:17 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12ecd03d308190a2fd9d42bf261172`	completed	May 24, 2026, 12:19 p.m.
NEDg	Description generation	`batch_6a12ed8aade48190bbec489801889492`	completed	May 24, 2026, 12:22 p.m.
NED2	Entity disambiguation (via description)	`batch_6a12ee4f57508190aa0d1832b30a9556`	completed	May 24, 2026, 12:25 p.m.
PD	Predicate disambiguation	`batch_69f64cacd2c08190aed8a1761d0da679`	completed	May 2, 2026, 7:12 p.m.

Created at: April 27, 2026, 5:33 p.m.