Triple

T3754667
Position Surface form Disambiguated ID Type / Status
Subject Early County, Georgia E82016 entity
Predicate namedAfter P63 FINISHED
Object Peter Early
Peter Early was an American politician and jurist who served as governor of Georgia in the early 19th century.
 E385595 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: Peter Early | Statement: [Early County, Georgia, namedAfter, Peter Early]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Peter Early
Context triple: [Early County, Georgia, namedAfter, Peter Early]
  • A. Peter Blaker
    Peter Blaker was a British Conservative politician who served in several ministerial roles, particularly in defense and foreign affairs, during the 1970s and early 1980s.
  • B. Peter Mathieson
    Peter Mathieson is a British nephrologist and academic leader who has served as principal and vice-chancellor of major universities, including the University of Edinburgh and the University of Hong Kong.
  • C. Peter Garnsey
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • D. Paul Millspaugh
    Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
  • E. Ted Oakes
    Ted Oakes is a wildlife television producer and filmmaker known for creating high-profile nature documentaries.
  • 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: Peter Early
Triple: [Early County, Georgia, namedAfter, Peter Early]
Generated description
Peter Early was an American politician and jurist who served as governor of Georgia in the early 19th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Peter Early
Target entity description: Peter Early was an American politician and jurist who served as governor of Georgia in the early 19th century.
  • A. Peter Blaker
    Peter Blaker was a British Conservative politician who served in several ministerial roles, particularly in defense and foreign affairs, during the 1970s and early 1980s.
  • B. Peter Mathieson
    Peter Mathieson is a British nephrologist and academic leader who has served as principal and vice-chancellor of major universities, including the University of Edinburgh and the University of Hong Kong.
  • C. Peter Garnsey
    Peter Garnsey is a prominent historian of the ancient world, particularly known for his influential scholarship on the social, economic, and legal history of the Roman Empire.
  • D. Paul Millspaugh
    Paul Millspaugh is a film editor known for his work on the romantic comedy "Two Can Play That Game."
  • E. Ted Oakes
    Ted Oakes is a wildlife television producer and filmmaker known for creating high-profile nature documentaries.
  • 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_69ad8b1db40081908b61ffa6b78afd4d completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcb94ffc08190a7fd1ce71a15f787 completed March 8, 2026, 7:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e50825588190b620950ac1d4f408 completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e5cd3b3c8190af6ca28a6772625c completed March 14, 2026, 4:36 a.m.
NED2 Entity disambiguation (via description) batch_69b4e671e02c819094cae2a3a2abb1b4 completed March 14, 2026, 4:39 a.m.
Created at: March 8, 2026, 3:35 p.m.