Triple

T8093949
Position Surface form Disambiguated ID Type / Status
Subject Apache Flink E188935 entity
Predicate hasComponent P35 FINISHED
Object Dispatcher
Dispatcher is a core Apache Flink component responsible for managing and coordinating job submissions and executions across the cluster.
 E711830 NE FINISHED

How this triple was built (4 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: Dispatcher | Statement: [Apache Flink, hasComponent, Dispatcher]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dispatcher
Context triple: [Apache Flink, hasComponent, Dispatcher]
  • A. Envoy
    Envoy is a high-performance, cloud-native edge and service proxy designed for microservices architectures, widely used for load balancing, observability, and service mesh infrastructure.
  • B. Recetor
    Recetor is a small rural municipality located in the Casanare Department of eastern Colombia, known for its agricultural and livestock-based economy.
  • C. Dozier
    Dozier is a small town located in Crenshaw County in the state of Alabama, United States.
  • D. Dextre
    Dextre is a two-armed robotic handyman on the International Space Station designed to perform delicate maintenance tasks and reduce the need for spacewalks.
  • E. Dockery
    Dockery is an English surname most notably associated with actress Michelle Dockery, known for her role in the television series "Downton Abbey."
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
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: Dispatcher
Triple: [Apache Flink, hasComponent, Dispatcher]
Generated description
Dispatcher is a core Apache Flink component responsible for managing and coordinating job submissions and executions across the cluster.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dispatcher
Target entity description: Dispatcher is a core Apache Flink component responsible for managing and coordinating job submissions and executions across the cluster.
  • A. Envoy
    Envoy is a high-performance, cloud-native edge and service proxy designed for microservices architectures, widely used for load balancing, observability, and service mesh infrastructure.
  • B. Recetor
    Recetor is a small rural municipality located in the Casanare Department of eastern Colombia, known for its agricultural and livestock-based economy.
  • C. Dozier
    Dozier is a small town located in Crenshaw County in the state of Alabama, United States.
  • D. Dextre
    Dextre is a two-armed robotic handyman on the International Space Station designed to perform delicate maintenance tasks and reduce the need for spacewalks.
  • E. Dockery
    Dockery is an English surname most notably associated with actress Michelle Dockery, known for her role in the television series "Downton Abbey."
  • F. None of above. chosen

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_69ca82b7b3e88190b9041ab0ef28b3cb completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb429089cc81909e4625f9cc7e305f completed March 31, 2026, 3:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69cc64112138819096050975d707d8ee completed April 1, 2026, 12:17 a.m.
NEDg Description generation batch_69cc68647cec81909736383fbe73d2e8 completed April 1, 2026, 12:35 a.m.
NED2 Entity disambiguation (via description) batch_69cc69b93bbc8190be2338182dd57b17 completed April 1, 2026, 12:41 a.m.
Created at: March 30, 2026, 5:30 p.m.