Triple

T2103534
Position Surface form Disambiguated ID Type / Status
Subject Albuquerque E37142 entity
Predicate hasNickname P39 FINISHED
Object Duke City
Duke City is a popular nickname for Albuquerque, New Mexico, reflecting its historical ties to Spanish nobility.
E233730 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: Duke City | Statement: [Albuquerque, hasNickname, Duke City]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Duke City
Context triple: [Albuquerque, hasNickname, Duke City]
  • A. Charlotte
    Charlotte is a royal figure bearing the traditional title of Princess Royal, historically associated with the eldest daughter of the British monarch.
  • B. Charlotte
    Charlotte is a feminine given name of French and English origin, traditionally used as the female form of Charles and borne by numerous queens, nobles, and notable figures.
  • C. San Antonio
    San Antonio is a major Chilean port city known for its significant role in the country’s maritime trade and fishing industries.
  • D. San Antonio
    San Antonio was one of the ships in Ferdinand Magellan’s expedition fleet that participated in the first circumnavigation attempt of the globe.
  • E. San Antonio
    San Antonio is a large, historic city in south-central Texas known for the Alamo, the River Walk, and its rich blend of Mexican and Texan culture.
  • 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: Duke City
Triple: [Albuquerque, hasNickname, Duke City]
Generated description
Duke City is a popular nickname for Albuquerque, New Mexico, reflecting its historical ties to Spanish nobility.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Duke City
Target entity description: Duke City is a popular nickname for Albuquerque, New Mexico, reflecting its historical ties to Spanish nobility.
  • A. Charlotte
    Charlotte is a royal figure bearing the traditional title of Princess Royal, historically associated with the eldest daughter of the British monarch.
  • B. Charlotte
    Charlotte is a feminine given name of French and English origin, traditionally used as the female form of Charles and borne by numerous queens, nobles, and notable figures.
  • C. San Antonio
    San Antonio is a large, historic city in south-central Texas known for the Alamo, the River Walk, and its rich blend of Mexican and Texan culture.
  • D. San Antonio
    San Antonio was one of the ships in Ferdinand Magellan’s expedition fleet that participated in the first circumnavigation attempt of the globe.
  • E. San Antonio
    San Antonio is a major Chilean port city known for its significant role in the country’s maritime trade and fishing industries.
  • 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_69a8861828948190924aa30c08806b3a completed March 4, 2026, 7:20 p.m.
NER Named-entity recognition batch_69abbabf7cdc81909636dff34badc1c5 completed March 7, 2026, 5:42 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae3068189c81909cf76fd1fc2a0fe6 completed March 9, 2026, 2:28 a.m.
NEDg Description generation batch_69ae30f9d0448190a0b3251676d9825d completed March 9, 2026, 2:31 a.m.
NED2 Entity disambiguation (via description) batch_69ae316398488190b9dd38145d5488b4 completed March 9, 2026, 2:33 a.m.
Created at: March 4, 2026, 7:43 p.m.