Triple

T15008352
Position Surface form Disambiguated ID Type / Status
Subject Max Greenfield E377767 entity
Predicate hasSurname P18 FINISHED
Object Greenfield
Greenfield is a common English-language surname borne by various notable individuals across fields such as entertainment, politics, and academia.
 E377767 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: Greenfield | Statement: [Max Greenfield, hasSurname, Greenfield]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Greenfield
Context triple: [Max Greenfield, hasSurname, Greenfield]
  • A. Greenfield
    Greenfield is a village in the Saddleworth area of Oldham, Greater Manchester, England, situated on the edge of the Pennines.
  • B. Greenfield
    Greenfield is a town located within Saratoga County in the state of New York, United States.
  • C. Greenfield
    Greenfield is a primarily residential neighborhood in Pittsburgh, Pennsylvania, known for its hilly streets, tight-knit community, and proximity to major city parks and universities.
  • D. Greenfield
    Greenfield is a small city in central Indiana that serves as a suburban community within the greater Indianapolis metropolitan area.
  • E. Greenfield
    Greenfield is a suburban city in southeastern Wisconsin, located just southwest of Milwaukee.
  • 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: Greenfield
Triple: [Max Greenfield, hasSurname, Greenfield]
Generated description
Greenfield is a common English-language surname borne by various notable individuals across fields such as entertainment, politics, and academia.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Greenfield
Target entity description: Greenfield is a common English-language surname borne by various notable individuals across fields such as entertainment, politics, and academia.
  • A. Greenfield chosen
    Greenfield is a surname most notably associated with American actor Max Greenfield, known for his television and film roles.
  • B. Greenfield
    Greenfield is a primarily residential neighborhood in Pittsburgh, Pennsylvania, known for its hilly streets, tight-knit community, and proximity to major city parks and universities.
  • C. Greenfield
    Greenfield is a suburban city in southeastern Wisconsin, located just southwest of Milwaukee.
  • D. Greenfield
    Greenfield is a small city in central Indiana that serves as a suburban community within the greater Indianapolis metropolitan area.
  • E. Greenfield
    Greenfield is a village in the Saddleworth area of Oldham, Greater Manchester, England, situated on the edge of the Pennines.
  • 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_69d85cd3a3c881908c71fc424d459c17 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69ded73348d4819091d9e7f1b0fed822 completed April 15, 2026, 12:09 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe96a52bb08190961e3f18d751fe2a completed May 9, 2026, 2:06 a.m.
NEDg Description generation batch_69fe98bf505c819089740180a763db34 completed May 9, 2026, 2:15 a.m.
NED2 Entity disambiguation (via description) batch_69fe9aab47888190812ff9732380e124 completed May 9, 2026, 2:23 a.m.
Created at: April 10, 2026, 2:55 a.m.