Triple

T7651020
Position Surface form Disambiguated ID Type / Status
Subject Hannes Alfvén E173250 entity
Predicate givenName P17 FINISHED
Object Hannes
Hannes is a masculine given name of Germanic origin, commonly used in Scandinavian and German-speaking countries as a form of Johannes.
 E75878 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: Hannes | Statement: [Hannes Alfvén, givenName, Hannes]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Hannes
Context triple: [Hannes Alfvén, givenName, Hannes]
  • A. Hans
    Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
  • B. Sven
    Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • C. Hansi
    Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
  • D. Mathias
    Mathias is a surname most notably associated with Bob Mathias, the American decathlete and two-time Olympic gold medalist.
  • E. Johan
    Johan is a masculine given name of Scandinavian origin, commonly used in countries such as Norway, Sweden, and Denmark.
  • 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: Hannes
Triple: [Hannes Alfvén, givenName, Hannes]
Generated description
Hannes is a masculine given name of Germanic origin, commonly used in Scandinavian and German-speaking countries as a form of Johannes.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Hannes
Target entity description: Hannes is a masculine given name of Germanic origin, commonly used in Scandinavian and German-speaking countries as a form of Johannes.
  • A. Hans chosen
    Hans is a masculine given name of Germanic origin commonly used in Germanic and Scandinavian countries.
  • B. Sven
    Sven is the lovable reindeer companion in Disney's animated film "Frozen," known for his close bond with Kristoff and his expressive, dog-like personality.
  • C. Hansi
    Hansi is a historic town in the Hisar district of Haryana, India, known for its ancient forts and archaeological significance.
  • D. Mathias
    Mathias is a surname most notably associated with Bob Mathias, the American decathlete and two-time Olympic gold medalist.
  • E. Johan
    Johan is the given first name of J. Erik Jonsson, an American businessman and philanthropist who co-founded Texas Instruments and served as mayor of Dallas.
  • F. None of above.

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_69c6995473348190a4f41d110d619a18 completed March 27, 2026, 2:51 p.m.
NER Named-entity recognition batch_69c70175e4b88190bc40c839a42180d4 completed March 27, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69c8a21b1ca881908ee73a418d4069a7 completed March 29, 2026, 3:52 a.m.
NEDg Description generation batch_69c8a2dd5d308190b8a463f0cd5c0f78 completed March 29, 2026, 3:56 a.m.
NED2 Entity disambiguation (via description) batch_69c8a33a9cb08190b6943ad387f83cab completed March 29, 2026, 3:57 a.m.
Created at: March 27, 2026, 3:58 p.m.