Triple

T4177266
Position Surface form Disambiguated ID Type / Status
Subject ACM SIGSOFT Outstanding Research Award E86506 entity
Predicate hasRecipient P108 FINISHED
Object Jeff Kramer
Jeff Kramer is a prominent computer scientist recognized for his influential contributions to software engineering research.
 E424290 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: Jeff Kramer | Statement: [ACM SIGSOFT Outstanding Research Award, hasRecipient, Jeff Kramer]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Jeff Kramer
Context triple: [ACM SIGSOFT Outstanding Research Award, hasRecipient, Jeff Kramer]
  • A. Ted Kramer
    Ted Kramer is the work-obsessed advertising executive and father whose struggle to raise his young son alone after his wife leaves him forms the emotional core of the film "Kramer vs. Kramer."
  • B. John Kamps
    John Kamps is an American screenwriter best known for co-writing the family science fiction film "Zathura: A Space Adventure."
  • C. Larry Kellner
    Larry Kellner is an American business executive best known for leading Continental Airlines as its chief executive officer.
  • D. Joe Klotz
    Joe Klotz is an American film editor best known for his acclaimed work on the drama film "Precious."
  • E. John Eisendrath
    John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
  • 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: Jeff Kramer
Triple: [ACM SIGSOFT Outstanding Research Award, hasRecipient, Jeff Kramer]
Generated description
Jeff Kramer is a prominent computer scientist recognized for his influential contributions to software engineering research.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Jeff Kramer
Target entity description: Jeff Kramer is a prominent computer scientist recognized for his influential contributions to software engineering research.
  • A. Ted Kramer
    Ted Kramer is the work-obsessed advertising executive and father whose struggle to raise his young son alone after his wife leaves him forms the emotional core of the film "Kramer vs. Kramer."
  • B. John Kamps
    John Kamps is an American screenwriter best known for co-writing the family science fiction film "Zathura: A Space Adventure."
  • C. Larry Kellner
    Larry Kellner is an American business executive best known for leading Continental Airlines as its chief executive officer.
  • D. Joe Klotz
    Joe Klotz is an American film editor best known for his acclaimed work on the drama film "Precious."
  • E. John Eisendrath
    John Eisendrath is a television writer and producer best known for his work on series such as "The Blacklist" and "Alias."
  • 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_69aed93de98c8190ad838ce507b77c8a completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69af02ec20fc8190b6f30576337e0ddc completed March 9, 2026, 5:27 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5a84713f88190a051b7d94b89d585 completed March 14, 2026, 6:26 p.m.
NEDg Description generation batch_69b5ac38488481908e357c35fbdcbc60 completed March 14, 2026, 6:43 p.m.
NED2 Entity disambiguation (via description) batch_69b5acc576308190a5e34b580db46944 completed March 14, 2026, 6:45 p.m.
Created at: March 9, 2026, 3:45 p.m.