Triple

T7984805
Position Surface form Disambiguated ID Type / Status
Subject Apache Spark E185661 entity
Predicate component P35 FINISHED
Object GraphX
GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
 E705279 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: GraphX | Statement: [Apache Spark, component, GraphX]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: GraphX
Context triple: [Apache Spark, component, GraphX]
  • A. DGL
    DGL is the vehicle registration code assigned to the town of Głogów in Poland.
  • B. NetworkX
    NetworkX is a Python library for creating, analyzing, and visualizing complex networks and graphs.
  • C. Graph Algorithms (book)
    "Graph Algorithms" is a foundational textbook by Shimon Even that systematically presents the theory, design, and analysis of algorithms for solving fundamental problems on graphs.
  • D. BGL
    BGL is the vehicle registration code for the Berchtesgadener Land district in the German state of Bavaria.
  • E. DAG
    DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
  • 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: GraphX
Triple: [Apache Spark, component, GraphX]
Generated description
GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: GraphX
Target entity description: GraphX is Apache Spark’s distributed graph processing framework that enables large-scale graph computation and analysis using Spark’s resilient distributed datasets (RDDs).
  • A. DGL
    DGL is the vehicle registration code assigned to the town of Głogów in Poland.
  • B. NetworkX
    NetworkX is a Python library for creating, analyzing, and visualizing complex networks and graphs.
  • C. Graph Algorithms (book)
    "Graph Algorithms" is a foundational textbook by Shimon Even that systematically presents the theory, design, and analysis of algorithms for solving fundamental problems on graphs.
  • D. BGL
    BGL is the vehicle registration code for the Berchtesgadener Land district in the German state of Bavaria.
  • E. DAG
    DAG is the National Rail station code for Dalgety Bay railway station in Fife, Scotland.
  • 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_69ca829a2cfc819083d591d58ec04075 completed March 30, 2026, 2:03 p.m.
NER Named-entity recognition batch_69cb3c4a55b881909a96133e56c0dffa completed March 31, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69cbe0e0b2748190930c22c6157d1b07 completed March 31, 2026, 2:57 p.m.
NEDg Description generation batch_69cc46c221848190848c7e017e532a16 completed March 31, 2026, 10:12 p.m.
NED2 Entity disambiguation (via description) batch_69cc480d2f40819085046a1d0c9d05e0 completed March 31, 2026, 10:17 p.m.
Created at: March 30, 2026, 5:15 p.m.