Triple

T574731
Position Surface form Disambiguated ID Type / Status
Subject SIGKDD E13737 entity
Predicate flagshipEvent P6628 FINISHED
Object KDD conference
The KDD conference is a premier international research conference focused on knowledge discovery and data mining, bringing together experts in data science, machine learning, and big data.
E13737 NE FINISHED

How this triple was built (5 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: KDD conference | Statement: [SIGKDD, flagshipEvent, KDD conference]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: KDD conference
Context triple: [SIGKDD, flagshipEvent, KDD conference]
  • A. SIGKDD
    SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
  • B. ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
  • C. ACM Transactions on Data Science
    ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
  • D. SIGKDD Innovation Award
    The SIGKDD Innovation Award is a premier annual honor in the data mining and knowledge discovery community recognizing influential, long-lasting technical contributions to the field.
  • E. SIGKDD Service Award
    The SIGKDD Service Award is a prestigious annual honor recognizing individuals who have made exceptional contributions to the data mining and knowledge discovery community through dedicated professional service and leadership.
  • 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: KDD conference
Triple: [SIGKDD, flagshipEvent, KDD conference]
Generated description
The KDD conference is a premier international research conference focused on knowledge discovery and data mining, bringing together experts in data science, machine learning, and big data.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: KDD conference
Target entity description: The KDD conference is a premier international research conference focused on knowledge discovery and data mining, bringing together experts in data science, machine learning, and big data.
  • A. SIGKDD chosen
    SIGKDD is the ACM Special Interest Group on Knowledge Discovery and Data Mining, best known for its flagship KDD conference and contributions to data mining and machine learning research.
  • B. ACM Transactions on Knowledge Discovery from Data
    ACM Transactions on Knowledge Discovery from Data is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data mining, knowledge discovery, and related areas of data science and machine learning.
  • C. ACM Transactions on Data Science
    ACM Transactions on Data Science is a peer-reviewed scholarly journal published by the Association for Computing Machinery that focuses on research in data science, including theory, methods, and applications.
  • D. SIGKDD Innovation Award
    The SIGKDD Innovation Award is a premier annual honor in the data mining and knowledge discovery community recognizing influential, long-lasting technical contributions to the field.
  • E. SIGKDD Service Award
    The SIGKDD Service Award is a prestigious annual honor recognizing individuals who have made exceptional contributions to the data mining and knowledge discovery community through dedicated professional service and leadership.
  • F. None of above.
PD Predicate disambiguation gpt-5-mini-2025-08-07
Target predicate: flagshipEvent
Context triple: [SIGKDD, flagshipEvent, KDD conference]
  • A. flagshipFor
    Indicates that one entity serves as the primary or leading representative example for another entity, often embodying its most important qualities or status.
  • B. flagshipProgram
    Indicates that a program is the primary, most prominent, or most important offering associated with an entity.
  • C. flagshipPeriod
    Indicates the time span during which an entity serves as the primary or leading representative (flagship) within a given context or organization.
  • D. flagshipStatus
    Indicates that an entity holds the primary or most prominent position within a group, organization, or collection, often serving as its leading or representative example.
  • E. featuresEvent chosen
    Indicates that an entity includes, presents, or highlights a particular event as part of its content or offering.
  • 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_69a4933fa4d88190a7949cc83c08c5c1 completed March 1, 2026, 7:27 p.m.
NER Named-entity recognition batch_69a49b4c23548190a3b883239c7c78c8 completed March 1, 2026, 8:02 p.m.
NED1 Entity disambiguation (via context triple) batch_69a50e23bce481908404040b848ba9c1 completed March 2, 2026, 4:12 a.m.
NEDg Description generation batch_69a50ed652148190a84c0d2aa5307211 completed March 2, 2026, 4:15 a.m.
NED2 Entity disambiguation (via description) batch_69a50f53c16c8190bbeb7fa550ed46b1 completed March 2, 2026, 4:17 a.m.
PD Predicate disambiguation batch_69a494c4969c819080375d08f9eec50c completed March 1, 2026, 7:34 p.m.
Created at: March 1, 2026, 7:33 p.m.