Triple

T1717797
Position Surface form Disambiguated ID Type / Status
Subject Kali Linux E37327 entity
Predicate includesTool P1393 FINISHED
Object Responder
Responder is a network analysis and credential-harvesting tool commonly used in penetration testing to capture and manipulate authentication traffic on local networks.
 E192910 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: Responder | Statement: [Kali Linux, includesTool, Responder]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Responder
Context triple: [Kali Linux, includesTool, Responder]
  • A. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • B. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • C. RE
    RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
  • D. Ren
    Ren is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her experiences as a sex worker and survivor in a bioengineered, post-apocalyptic world.
  • E. Repre
    Repre is the popular nickname for the Slovakia men's national ice hockey team, used by fans and media to refer to the national squad.
  • 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: Responder
Triple: [Kali Linux, includesTool, Responder]
Generated description
Responder is a network analysis and credential-harvesting tool commonly used in penetration testing to capture and manipulate authentication traffic on local networks.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Responder
Target entity description: Responder is a network analysis and credential-harvesting tool commonly used in penetration testing to capture and manipulate authentication traffic on local networks.
  • A. RE
    RE is the common abbreviation for the British Army’s Corps of Royal Engineers, responsible for military engineering and technical support.
  • B. RE
    RE is the abbreviation for RegioExpress, a category of regional express trains commonly used in European rail transport.
  • C. RE
    RE is the two-letter ISO 3166-1 alpha-2 country code assigned to the French overseas department and region of Réunion.
  • D. Ren
    Ren is a central character in Margaret Atwood’s dystopian MaddAddam trilogy, known for her experiences as a sex worker and survivor in a bioengineered, post-apocalyptic world.
  • E. Repre
    Repre is the popular nickname for the Slovakia men's national ice hockey team, used by fans and media to refer to the national squad.
  • 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_69a8861912dc8190931af43b4b9158a7 completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69aa6337d8408190bdba8b50652d50ae completed March 6, 2026, 5:16 a.m.
NED1 Entity disambiguation (via context triple) batch_69ad8ae6940c81909c1ebdfb0cdef5fc completed March 8, 2026, 2:42 p.m.
NEDg Description generation batch_69ad957adf1c8190b7c8656c1984f998 completed March 8, 2026, 3:27 p.m.
NED2 Entity disambiguation (via description) batch_69ad97af6b388190b2af293599108df3 completed March 8, 2026, 3:37 p.m.
Created at: March 4, 2026, 7:30 p.m.