Triple

T17346469
Position Surface form Disambiguated ID Type / Status
Subject Frank Henry Sommer Professor of Law E421699 entity
Predicate namedAfter P63 FINISHED
Object Frank Henry Sommer — NE ONDG

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: Frank Henry Sommer | Statement: [Frank Henry Sommer Professor of Law, namedAfter, Frank Henry Sommer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Frank Henry Sommer
Context triple: [Frank Henry Sommer Professor of Law, namedAfter, Frank Henry Sommer]
  • A. Frank H. Sommer
    Frank H. Sommer was an American football player and coach associated with early 20th-century collegiate football.
  • B. Bernard J. Rothlein
    Bernard J. Rothlein is an American engineer and entrepreneur best known as one of the founders of the semiconductor company National Semiconductor.
  • C. Philip B. Heymann
    Philip B. Heymann was an American legal scholar and former high-ranking U.S. Justice Department official known for his work in criminal law, national security, and public service.
  • D. Allen G. Siegler
    Allen G. Siegler was an American cinematographer active during the early to mid-20th century, known for his work on numerous Hollywood films.
  • E. Harry Julian Fink
    Harry Julian Fink was an American screenwriter best known for co-creating the iconic tough cop character Harry Callahan in the film "Dirty Harry."
  • 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: Frank Henry Sommer
Triple: [Frank Henry Sommer Professor of Law, namedAfter, Frank Henry Sommer]
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Frank Henry Sommer
Target entity description: Frank Henry Sommer was a distinguished legal scholar and educator whose legacy is honored through an endowed professorship in law.
  • A. Frank H. Sommer
    Frank H. Sommer was an American football player and coach associated with early 20th-century collegiate football.
  • B. Bernard J. Rothlein
    Bernard J. Rothlein is an American engineer and entrepreneur best known as one of the founders of the semiconductor company National Semiconductor.
  • C. Philip B. Heymann
    Philip B. Heymann was an American legal scholar and former high-ranking U.S. Justice Department official known for his work in criminal law, national security, and public service.
  • D. Allen G. Siegler
    Allen G. Siegler was an American cinematographer active during the early to mid-20th century, known for his work on numerous Hollywood films.
  • E. Harry Julian Fink
    Harry Julian Fink was an American screenwriter best known for co-creating the iconic tough cop character Harry Callahan in the film "Dirty Harry."
  • F. None of above. chosen

Provenance (4 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_69d889d520008190a26917a95bf1c2ea completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e43a2923b48190a5d1abd3f535c59f completed April 19, 2026, 2:12 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0195546198819085804ec0b5b18040 completed May 11, 2026, 8:37 a.m.
NEDg Description generation batch_6a01965807cc819088792a88b8a099d3 in_progress May 11, 2026, 8:42 a.m.
Created at: April 10, 2026, 5:44 a.m.