Triple

T14910366
Position Surface form Disambiguated ID Type / Status
Subject LaBahn Arena E371244 entity
Predicate namedAfter P63 FINISHED
Object Mary Ann LaBahn
Mary Ann LaBahn is a benefactor and namesake associated with the University of Wisconsin's LaBahn Arena, recognized for her significant support of the institution's athletics programs.
E1329214 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: Mary Ann LaBahn | Statement: [LaBahn Arena, namedAfter, Mary Ann LaBahn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mary Ann LaBahn
Context triple: [LaBahn Arena, namedAfter, Mary Ann LaBahn]
  • A. Mary Patricia Plangman
    Mary Patricia Plangman is the birth name of Patricia Highsmith, the acclaimed American novelist best known for her psychological thrillers and the Tom Ripley series.
  • B. Ann Lembeck
    Ann Lembeck is the wife of American actor and comedian Denis Leary.
  • C. Mary M. Wyman
    Mary M. Wyman was the wife of influential American geographer and geologist William Morris Davis.
  • D. Mary R. Haas
    Mary R. Haas was an influential American linguist renowned for her work on Native American languages and for training a generation of field linguists.
  • E. Mary M. Schroeder
    Mary M. Schroeder is an American jurist who served as a judge, and later chief judge, on the U.S. Court of Appeals for the Ninth Circuit.
  • 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: Mary Ann LaBahn
Triple: [LaBahn Arena, namedAfter, Mary Ann LaBahn]
Generated description
Mary Ann LaBahn is a benefactor and namesake associated with the University of Wisconsin's LaBahn Arena, recognized for her significant support of the institution's athletics programs.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mary Ann LaBahn
Target entity description: Mary Ann LaBahn is a benefactor and namesake associated with the University of Wisconsin's LaBahn Arena, recognized for her significant support of the institution's athletics programs.
  • A. Mary Patricia Plangman
    Mary Patricia Plangman is the birth name of Patricia Highsmith, the acclaimed American novelist best known for her psychological thrillers and the Tom Ripley series.
  • B. Ann Lembeck
    Ann Lembeck is the wife of American actor and comedian Denis Leary.
  • C. Mary M. Wyman
    Mary M. Wyman was the wife of influential American geographer and geologist William Morris Davis.
  • D. Mary R. Haas
    Mary R. Haas was an influential American linguist renowned for her work on Native American languages and for training a generation of field linguists.
  • E. Mary M. Schroeder
    Mary M. Schroeder is an American jurist who served as a judge, and later chief judge, on the U.S. Court of Appeals for the Ninth Circuit.
  • 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_69d85cc7ea3481908228b5acb7d06f12 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded61c6b9c8190a92934d49b98fe46 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0491547b9081909876f88fe6a58ee7 completed May 13, 2026, 2:57 p.m.
NEDg Description generation batch_6a04922aa908819093d00f57fdb00627 completed May 13, 2026, 3 p.m.
NED2 Entity disambiguation (via description) batch_6a04933727488190b7a04789a17a16a0 completed May 13, 2026, 3:05 p.m.
Created at: April 10, 2026, 2:26 a.m.