Triple

T50421
Position Surface form Disambiguated ID Type / Status
Subject House of Orange-Nassau E990 entity
Predicate originatesFrom P26 FINISHED
Object 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.
 E3952 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: [House of Orange-Nassau, originatesFrom, Orange]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orange
Context triple: [House of Orange-Nassau, originatesFrom, Orange]
  • A. Crimson
    Crimson is the collective name for Harvard University's varsity athletic teams competing in collegiate sports.
  • B. Yale Blue
    Yale Blue is a deep, rich shade of blue traditionally associated with academic institutions and collegiate branding.
  • C. Green Light
    "Green Light" is an upbeat, dance-pop and R&B single by John Legend featuring André 3000, known for its energetic tempo and departure from Legend’s usual soulful ballad style.
  • D. Bluebonnet
    Bluebonnet is a vibrant blue wildflower, especially known for carpeting Texas fields each spring and serving as a symbol of the state's natural beauty.
  • E. Rogers
    Rogers is a common English-language surname borne by numerous notable individuals across fields such as science, politics, entertainment, and sports.
  • 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: [House of Orange-Nassau, originatesFrom, Orange]
Generated description
Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orange
Target entity description: Orange is a historic town in southeastern France best known for giving its name and origin to the Dutch royal House of Orange-Nassau.
  • A. Crimson
    Crimson is the collective name for Harvard University's varsity athletic teams competing in collegiate sports.
  • B. Yale Blue
    Yale Blue is a deep, rich shade of blue traditionally associated with academic institutions and collegiate branding.
  • C. Green Light
    "Green Light" is an upbeat, dance-pop and R&B single by John Legend featuring André 3000, known for its energetic tempo and departure from Legend’s usual soulful ballad style.
  • D. Black
    Black is a nominative report series of early United States Supreme Court decisions compiled and published under the name of the court reporter Black.
  • E. Clementine
    Clementine is a feminine given name most famously borne by Clementine Churchill, the wife of British Prime Minister Winston Churchill.
  • 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_69a2480baefc81909951b14058479aa2 completed Feb. 28, 2026, 1:42 a.m.
NER Named-entity recognition batch_69a24af56cc88190a898f8bf2a283820 completed Feb. 28, 2026, 1:55 a.m.
NED1 Entity disambiguation (via context triple) batch_69a24e659ac48190a11b70a85867d784 completed Feb. 28, 2026, 2:09 a.m.
NEDg Description generation batch_69a24eff3f0881909b46502175682d99 completed Feb. 28, 2026, 2:12 a.m.
NED2 Entity disambiguation (via description) batch_69a2542d9b388190bcc4581c3b79aa51 completed Feb. 28, 2026, 2:34 a.m.
Created at: Feb. 28, 2026, 1:47 a.m.