Triple

T10158593
Position Surface form Disambiguated ID Type / Status
Subject Usenet E233830 entity
Predicate relatedConcept P37 FINISHED
Object Netiquette
Netiquette is the set of informal rules and guidelines for polite, respectful, and appropriate behavior in online communication.
 E845116 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: Netiquette | Statement: [Usenet, relatedConcept, Netiquette]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Netiquette
Context triple: [Usenet, relatedConcept, Netiquette]
  • A. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • B. ET
    ET is the two-letter IATA airline designator assigned to Ethiopian Airlines, the flag carrier of Ethiopia.
  • C. ET
    ET is the time standard used on the east coast of North America, including major cities like New York and Toronto, switching between Eastern Standard Time (EST) and Eastern Daylight Time (EDT) seasonally.
  • D. Postel’s law
    Postel’s law is a design principle in computing and networking that advises systems to be conservative in what they send and liberal in what they accept, promoting robustness and interoperability.
  • E. Media Mail
    Media Mail is a discounted U.S. Postal Service shipping option specifically for sending educational materials and media such as books, sound recordings, and DVDs.
  • 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: Netiquette
Triple: [Usenet, relatedConcept, Netiquette]
Generated description
Netiquette is the set of informal rules and guidelines for polite, respectful, and appropriate behavior in online communication.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Netiquette
Target entity description: Netiquette is the set of informal rules and guidelines for polite, respectful, and appropriate behavior in online communication.
  • A. sns
    sns is the conventional alias used when importing Seaborn, a popular Python data visualization library built on top of Matplotlib.
  • B. ET
    ET is the two-letter IATA airline designator assigned to Ethiopian Airlines, the flag carrier of Ethiopia.
  • C. ET
    ET is the time standard used on the east coast of North America, including major cities like New York and Toronto, switching between Eastern Standard Time (EST) and Eastern Daylight Time (EDT) seasonally.
  • D. Postel’s law
    Postel’s law is a design principle in computing and networking that advises systems to be conservative in what they send and liberal in what they accept, promoting robustness and interoperability.
  • E. Media Mail
    Media Mail is a discounted U.S. Postal Service shipping option specifically for sending educational materials and media such as books, sound recordings, and DVDs.
  • 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_69ca848e80748190b91d1e04d35512c7 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cdec56d944819081bc6ea36c905ba2 completed April 2, 2026, 4:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d300b7b3108190a8e7581193c322be completed April 6, 2026, 12:39 a.m.
NEDg Description generation batch_69d302537a548190b211727dd124cba6 completed April 6, 2026, 12:46 a.m.
NED2 Entity disambiguation (via description) batch_69d3033245448190bcc802b7dbd274fc completed April 6, 2026, 12:49 a.m.
Created at: March 30, 2026, 9:09 p.m.