Triple

T14904441
Position Surface form Disambiguated ID Type / Status
Subject A32 E360090 entity
Predicate connectsCity P4245 FINISHED
Object Orange
Orange is a city in southern California’s Inland Empire region, known for its historic Old Towne district and well-preserved early-20th-century architecture.
 E1126728 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: [A32, connectsCity, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orange
Context triple: [A32, connectsCity, Orange]
  • A. Orange
    Orange was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • B. Orange
    Orange is a Japanese rock band known for performing the opening theme song of the animated series "Generator Rex."
  • 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: [A32, connectsCity, Orange]
Generated description
Orange is a city in southern California’s Inland Empire region, known for its historic Old Towne district and well-preserved early-20th-century architecture.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orange
Target entity description: Orange is a city in southern California’s Inland Empire region, known for its historic Old Towne district and well-preserved early-20th-century architecture.
  • 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 town in north-central Massachusetts known for its rural character, historic mill village roots, and location along the Millers River.
  • 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 was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • 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_69d827980cbc8190a0c569ae3940a1d9 completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69ded60cd5588190b1efecc2b220da69 completed April 15, 2026, 12:04 a.m.
NED1 Entity disambiguation (via context triple) batch_69fe72b4e4f88190af7e859d93dbbd28 completed May 8, 2026, 11:33 p.m.
NEDg Description generation batch_69fe7360c11481908e2e5127b466e31b completed May 8, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69fe743c37308190a045ef5f0ade8508 completed May 8, 2026, 11:39 p.m.
Created at: April 10, 2026, 2:12 a.m.