Triple

T3308971
Position Surface form Disambiguated ID Type / Status
Subject Hillsborough, North Carolina E69523 entity
Predicate originalName P65 FINISHED
Object Orange
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
 E345816 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: Orange | Statement: [Hillsborough, North Carolina, originalName, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orange
Context triple: [Hillsborough, North Carolina, originalName, Orange]
  • A. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • B. Orange
    Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
  • C. Orange
    Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • D. Orange
    Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
  • E. Orange
    Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
  • 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: Orange
Triple: [Hillsborough, North Carolina, originalName, Orange]
Generated description
Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orange
Target entity description: Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • A. Orange
    Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • B. Orange
    Orange is a regional city in the Central Tablelands of New South Wales, Australia, known for its cool-climate wines, agriculture, and growing tourism industry.
  • C. Orange
    Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
  • D. Orange
    Orange is a citrus-flavored sports drink variety known for its bright, tangy taste and association with energy and hydration.
  • E. Orange
    Orange is the nickname and primary identity of Syracuse University's athletic teams, especially its prominent men's basketball program.
  • 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_69ad859f218081909458d2cebbf57565 completed March 8, 2026, 2:20 p.m.
NER Named-entity recognition batch_69adb0e9f33c81909cff835a83e0a657 completed March 8, 2026, 5:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69b2f3edc0c081908a7f5c02fe18584b completed March 12, 2026, 5:12 p.m.
NEDg Description generation batch_69b2fa2ba81c8190bc86a855c59b7c2f completed March 12, 2026, 5:38 p.m.
NED2 Entity disambiguation (via description) batch_69b303a4cb4481908b56ee60765b5fac completed March 12, 2026, 6:19 p.m.
Created at: March 8, 2026, 3:11 p.m.