Triple

T4331188
Position Surface form Disambiguated ID Type / Status
Subject Keep America Great E96751 entity
Predicate hasAbbreviation P43 FINISHED
Object KAG
KAG is the abbreviation for "Keep America Great," a political campaign slogan associated with Donald Trump.
 E430837 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: KAG | Statement: [Keep America Great, hasAbbreviation, KAG]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: KAG
Context triple: [Keep America Great, hasAbbreviation, KAG]
  • A. KAGS
    KAGS is the ICAO airport code for Augusta Regional Airport, a public airport serving the Augusta, Georgia area in the United States.
  • B. Kaag
    Kaag is a small Dutch village in South Holland known for its island setting in the Kagerplassen lake area and its traditional water sports and boating culture.
  • C. KAN
    KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
  • D. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • E. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • 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: KAG
Triple: [Keep America Great, hasAbbreviation, KAG]
Generated description
KAG is the abbreviation for "Keep America Great," a political campaign slogan associated with Donald Trump.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: KAG
Target entity description: KAG is the abbreviation for "Keep America Great," a political campaign slogan associated with Donald Trump.
  • A. KAGS
    KAGS is the ICAO airport code for Augusta Regional Airport, a public airport serving the Augusta, Georgia area in the United States.
  • B. Kaag
    Kaag is a small Dutch village in South Holland known for its island setting in the Kagerplassen lake area and its traditional water sports and boating culture.
  • C. KAN
    KAN is the IATA airport code for Mallam Aminu Kano International Airport, a major airport serving Kano in northern Nigeria.
  • D. Ka
    Ka is the introspective poet and protagonist of Orhan Pamuk’s novel "Snow," whose return to Turkey and entanglement in political and personal conflicts drive the story’s exploration of faith, identity, and modernity.
  • E. Ka
    Ka was an early ancient Egyptian king of the First Dynasty period, known from tomb inscriptions at Abydos and considered one of the first rulers to use a royal serekh.
  • 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_69b34542fd908190b11b08faad8decfd completed March 12, 2026, 10:59 p.m.
NER Named-entity recognition batch_69b3514c39748190900e13e70ed8848c completed March 12, 2026, 11:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5d09fad588190b488012b4fc6cb8c completed March 14, 2026, 9:18 p.m.
NEDg Description generation batch_69b5d1614b008190bc98fac7b1029456 completed March 14, 2026, 9:21 p.m.
NED2 Entity disambiguation (via description) batch_69b5d1c2f77c8190942be9d23c2c9f6b completed March 14, 2026, 9:23 p.m.
Created at: March 12, 2026, 11:13 p.m.