Triple

T7937964
Position Surface form Disambiguated ID Type / Status
Subject OpenStack E184327 entity
Predicate messageQueue P33920 FINISHED
Object Kafka
Kafka is a distributed event streaming platform widely used for building real-time data pipelines and messaging systems.
E699739 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: Kafka | Statement: [OpenStack, messageQueue, Kafka]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Kafka
Context triple: [OpenStack, messageQueue, Kafka]
  • A. Kafka y sus precursores
    Kafka y sus precursores es un célebre ensayo de Jorge Luis Borges en el que analiza la obra de Franz Kafka a través de sus antecedentes literarios y la idea de que un autor puede crear a sus precursores.
  • B. Georg Kafka
    Georg Kafka was a member of the Kafka family and a relative of the renowned writer Franz Kafka.
  • C. Gabriele Kafka
    Gabriele Kafka was one of Franz Kafka’s sisters, a member of the Kafka family in early 20th-century Prague.
  • D. Franz Kafka
    Franz Kafka was a 20th-century Bohemian novelist and short-story writer whose surreal, existential works like "The Metamorphosis" and "The Trial" profoundly shaped modern literature.
  • E. Ottla Kafka
    Ottla Kafka was the youngest sister of writer Franz Kafka, known from his diaries and letters for their close relationship and her later persecution and death in the Holocaust.
  • 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: Kafka
Triple: [OpenStack, messageQueue, Kafka]
Generated description
Kafka is a distributed event streaming platform widely used for building real-time data pipelines and messaging systems.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Kafka
Target entity description: Kafka is a distributed event streaming platform widely used for building real-time data pipelines and messaging systems.
  • A. Kafka y sus precursores
    Kafka y sus precursores es un célebre ensayo de Jorge Luis Borges en el que analiza la obra de Franz Kafka a través de sus antecedentes literarios y la idea de que un autor puede crear a sus precursores.
  • B. Georg Kafka
    Georg Kafka was a member of the Kafka family and a relative of the renowned writer Franz Kafka.
  • C. Gabriele Kafka
    Gabriele Kafka was one of Franz Kafka’s sisters, a member of the Kafka family in early 20th-century Prague.
  • D. Franz Kafka
    Franz Kafka was a 20th-century Bohemian novelist and short-story writer whose surreal, existential works like "The Metamorphosis" and "The Trial" profoundly shaped modern literature.
  • E. Ottla Kafka
    Ottla Kafka was the youngest sister of writer Franz Kafka, known from his diaries and letters for their close relationship and her later persecution and death in the Holocaust.
  • 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_69ca8290c21c8190906a5ca6fe2b03c4 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb3aef2394819086eea1f6ab117aed completed March 31, 2026, 3:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69cb5c0a96ac819099ad30fb925eb329 completed March 31, 2026, 5:30 a.m.
NEDg Description generation batch_69cb7634f4dc8190b5e537f24bccd651 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb67e77a48190b93c6ba61becfac4 completed March 31, 2026, 11:56 a.m.
Created at: March 30, 2026, 5:08 p.m.