Triple

T7013977
Position Surface form Disambiguated ID Type / Status
Subject Baker County E162653 entity
Predicate hasHistoricTown P847 FINISHED
Object Greenhorn
Greenhorn is a tiny former gold-mining town in Oregon known as one of the state’s smallest and most remote historic communities.
 E634914 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: Greenhorn | Statement: [Baker County, hasHistoricTown, Greenhorn]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Greenhorn
Context triple: [Baker County, hasHistoricTown, Greenhorn]
  • A. Entered Apprentice
    Entered Apprentice is the first and introductory degree of Freemasonry, representing a candidate’s initial initiation into the Masonic fraternity.
  • B. Junior
    Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
  • C. Junior
    Junior is the protagonist of the novel "Love" by Toni Morrison, around whom the story’s complex relationships and themes of desire, memory, and power revolve.
  • D. Young Corn
    Young Corn is a 1931 Regionalist painting by American artist Grant Wood that depicts an idealized Midwestern rural landscape with rolling fields and farm buildings.
  • E. Newcom
    Newcom is a surname of English origin borne by various individuals, including figures such as film editor James E. Newcom.
  • 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: Greenhorn
Triple: [Baker County, hasHistoricTown, Greenhorn]
Generated description
Greenhorn is a tiny former gold-mining town in Oregon known as one of the state’s smallest and most remote historic communities.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Greenhorn
Target entity description: Greenhorn is a tiny former gold-mining town in Oregon known as one of the state’s smallest and most remote historic communities.
  • A. Entered Apprentice
    Entered Apprentice is the first and introductory degree of Freemasonry, representing a candidate’s initial initiation into the Masonic fraternity.
  • B. Junior
    Junior is a 1994 comedy film in which Arnold Schwarzenegger plays a scientist who becomes pregnant as part of an experimental fertility project.
  • C. Junior
    Junior is the protagonist of the novel "Love" by Toni Morrison, around whom the story’s complex relationships and themes of desire, memory, and power revolve.
  • D. Young Corn
    Young Corn is a 1931 Regionalist painting by American artist Grant Wood that depicts an idealized Midwestern rural landscape with rolling fields and farm buildings.
  • E. Newcom
    Newcom is a surname of English origin borne by various individuals, including figures such as film editor James E. Newcom.
  • 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_69c6885a127c8190867b059bdccf13ff completed March 27, 2026, 1:38 p.m.
NER Named-entity recognition batch_69c6dc59cbfc8190bba9ebd14143d43c completed March 27, 2026, 7:36 p.m.
NED1 Entity disambiguation (via context triple) batch_69c76a53e96081909dfe21a80b20f80d completed March 28, 2026, 5:42 a.m.
NEDg Description generation batch_69c76b5301748190b0780ae20504f22c completed March 28, 2026, 5:46 a.m.
NED2 Entity disambiguation (via description) batch_69c76c01679c8190b61f642c23c25ed5 completed March 28, 2026, 5:49 a.m.
Created at: March 27, 2026, 2:34 p.m.