Triple

T3132123
Position Surface form Disambiguated ID Type / Status
Subject Dany Heatley E65438 entity
Predicate nickname P55 FINISHED
Object Dany
Dany is a former professional ice hockey winger best known for his high-scoring NHL career, including multiple 50-goal seasons and a prominent role with the Ottawa Senators.
 E330481 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: Dany | Statement: [Dany Heatley, nickname, Dany]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Dany
Context triple: [Dany Heatley, nickname, Dany]
  • A. Dara
    Dara is a given name most prominently associated with Dara Khosrowshahi, the Iranian-American businessman and CEO of Uber.
  • B. Davo
    Davo is a common informal nickname or short form of the given name David, often used in English-speaking countries.
  • C. Denio
    Denio is a small unincorporated community in northern Nevada near the Oregon border, known for its remote high-desert setting and ranching heritage.
  • D. Dustin
    Dustin is a masculine given name commonly used in English-speaking countries.
  • E. Durkan
    Durkan is a surname most notably associated with Jenny Durkan, the former mayor of Seattle and an American attorney and politician.
  • 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: Dany
Triple: [Dany Heatley, nickname, Dany]
Generated description
Dany is a former professional ice hockey winger best known for his high-scoring NHL career, including multiple 50-goal seasons and a prominent role with the Ottawa Senators.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Dany
Target entity description: Dany is a former professional ice hockey winger best known for his high-scoring NHL career, including multiple 50-goal seasons and a prominent role with the Ottawa Senators.
  • A. Dara
    Dara is a given name most prominently associated with Dara Khosrowshahi, the Iranian-American businessman and CEO of Uber.
  • B. Davo
    Davo is a common informal nickname or short form of the given name David, often used in English-speaking countries.
  • C. Denio
    Denio is a small unincorporated community in northern Nevada near the Oregon border, known for its remote high-desert setting and ranching heritage.
  • D. Dustin
    Dustin is a masculine given name commonly used in English-speaking countries.
  • E. Durkan
    Durkan is a surname most notably associated with Jenny Durkan, the former mayor of Seattle and an American attorney and politician.
  • 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_69ad8581c25c8190b0d85ba9b9baa531 completed March 8, 2026, 2:19 p.m.
NER Named-entity recognition batch_69ada55f77b881908866fc43bdb18185 completed March 8, 2026, 4:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69b20f82787c81908eb72b18614c3421 completed March 12, 2026, 12:57 a.m.
NEDg Description generation batch_69b2102e35b08190ad9ca397f0c937da completed March 12, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_69b21458b07081909d75886e0d9f88e9 completed March 12, 2026, 1:18 a.m.
Created at: March 8, 2026, 3:04 p.m.