Triple

T17598630
Position Surface form Disambiguated ID Type / Status
Subject Anypoint Studio E428636 entity
Predicate supports P516 FINISHED
Object DataWeave
DataWeave is MuleSoft’s powerful data transformation language used to query, map, and convert data across formats within integration flows.
 E1278751 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: DataWeave | Statement: [Anypoint Studio, supports, DataWeave]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: DataWeave
Context triple: [Anypoint Studio, supports, DataWeave]
  • A. JSONiq
    JSONiq is a query and processing language designed specifically for JSON data, extending concepts from XQuery to work with hierarchical and semi-structured information.
  • B. Power Fx
    Power Fx is a low-code, Excel-like formula language developed by Microsoft for building logic and expressions in Power Platform applications.
  • C. XQuery
    XQuery is a functional query and programming language designed for extracting and manipulating data from XML documents and related data sources.
  • D. DSX
    DSX is a U.S. Air Force research spacecraft designed to conduct space environment and radiation experiments in medium Earth orbit.
  • E. BPEL
    BPEL (Business Process Execution Language) is an XML-based language used to define and orchestrate business processes and web services interactions in a standardized, executable form.
  • 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: DataWeave
Triple: [Anypoint Studio, supports, DataWeave]
Generated description
DataWeave is MuleSoft’s powerful data transformation language used to query, map, and convert data across formats within integration flows.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: DataWeave
Target entity description: DataWeave is MuleSoft’s powerful data transformation language used to query, map, and convert data across formats within integration flows.
  • A. JSONiq
    JSONiq is a query and processing language designed specifically for JSON data, extending concepts from XQuery to work with hierarchical and semi-structured information.
  • B. Power Fx
    Power Fx is a low-code, Excel-like formula language developed by Microsoft for building logic and expressions in Power Platform applications.
  • C. XQuery
    XQuery is a functional query and programming language designed for extracting and manipulating data from XML documents and related data sources.
  • D. DSX
    DSX is a U.S. Air Force research spacecraft designed to conduct space environment and radiation experiments in medium Earth orbit.
  • E. BPEL
    BPEL (Business Process Execution Language) is an XML-based language used to define and orchestrate business processes and web services interactions in a standardized, executable form.
  • 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_69d889e1030481909950e140c63255b9 completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e46c474e5481909d2736241b592dab completed April 19, 2026, 5:46 a.m.
NED1 Entity disambiguation (via context triple) batch_6a01e819cec88190b79a7e5c1ced3e18 completed May 11, 2026, 2:30 p.m.
NEDg Description generation batch_6a01eed32bbc81908ea9e2588e3741f8 completed May 11, 2026, 2:59 p.m.
NED2 Entity disambiguation (via description) batch_6a01ef6edb7c81908bdbf4d42dd6b9aa completed May 11, 2026, 3:02 p.m.
Created at: April 10, 2026, 5:51 a.m.