Triple

T7830053
Position Surface form Disambiguated ID Type / Status
Subject Jason Orange E181342 entity
Predicate familyName P18 FINISHED
Object Orange
Orange is a common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
E699196 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: [Jason Orange, familyName, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Orange
Context triple: [Jason Orange, familyName, 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 was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • D. 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.
  • E. Orange
    Orange is a small suburban village in Cuyahoga County, Ohio, known for its residential character and proximity to the Cleveland metropolitan area.
  • 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: [Jason Orange, familyName, Orange]
Generated description
Orange is a common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Orange
Target entity description: Orange is a common English surname of likely Norman or French origin, shared by various individuals including the British singer Jason Orange.
  • 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 was the original name of the town now known as Hillsborough in North Carolina, reflecting its early colonial-era identity.
  • C. Orange
    Orange is a major French multinational telecommunications company providing mobile, internet, and other digital services across numerous countries.
  • 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 one of the primary lines of the Washington Metro rapid transit system, serving multiple stations across the Washington, D.C. metropolitan area.
  • 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_69ca8282ccec819083c48efb72d21cf9 completed March 30, 2026, 2:02 p.m.
NER Named-entity recognition batch_69cb04ac013c81909533fa348776f50c completed March 30, 2026, 11:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69cb5a854fac8190802599615a0f7bc4 completed March 31, 2026, 5:24 a.m.
NEDg Description generation batch_69cb762ce6208190a24438e26ae83785 completed March 31, 2026, 7:22 a.m.
NED2 Entity disambiguation (via description) batch_69cbb615dc248190b57b4fc7c430517f completed March 31, 2026, 11:55 a.m.
Created at: March 30, 2026, 4:44 p.m.