Triple

T519669
Position Surface form Disambiguated ID Type / Status
Subject Felipe de Neve E10785 entity
Predicate familyName P18 FINISHED
Object de Neve
De Neve is a Spanish surname historically associated with notable figures such as colonial administrators and military officers in Spain and its former territories.
 E64681 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: de Neve | Statement: [Felipe de Neve, familyName, de Neve]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: de Neve
Context triple: [Felipe de Neve, familyName, de Neve]
  • A. Volnay
    Volnay is a renowned wine-producing village in Burgundy, France, celebrated for its elegant, aromatic red wines made primarily from Pinot Noir.
  • B. Neeleman
    Neeleman is a surname most notably associated with David Neeleman, the Brazilian-American entrepreneur and founder of multiple airlines including JetBlue Airways.
  • C. Vian
    Vian is a surname most notably associated with British Royal Navy Admiral Philip Vian, who served with distinction during both World Wars.
  • D. Senne
    The Senne is a small river flowing through Brussels, Belgium, much of which has been covered over as the city developed.
  • E. Nischel
    Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
  • 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: de Neve
Triple: [Felipe de Neve, familyName, de Neve]
Generated description
De Neve is a Spanish surname historically associated with notable figures such as colonial administrators and military officers in Spain and its former territories.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: de Neve
Target entity description: De Neve is a Spanish surname historically associated with notable figures such as colonial administrators and military officers in Spain and its former territories.
  • A. Volnay
    Volnay is a renowned wine-producing village in Burgundy, France, celebrated for its elegant, aromatic red wines made primarily from Pinot Noir.
  • B. Neeleman
    Neeleman is a surname most notably associated with David Neeleman, the Brazilian-American entrepreneur and founder of multiple airlines including JetBlue Airways.
  • C. Vian
    Vian is a surname most notably associated with British Royal Navy Admiral Philip Vian, who served with distinction during both World Wars.
  • D. Senne
    The Senne is a small river flowing through Brussels, Belgium, much of which has been covered over as the city developed.
  • E. Nischel
    Nischel is the local colloquial nickname for the large Karl Marx Monument in Chemnitz, Germany.
  • 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_69a2e84a0d08819087e01863fcd9abf1 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2f1a00a6c8190a62dc7c901c2f2ff completed Feb. 28, 2026, 1:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4a6feef408190b6e3f6fec95c36e7 completed March 1, 2026, 8:52 p.m.
NEDg Description generation batch_69a4a77d8f8481909de13ef3fc7dbbcb completed March 1, 2026, 8:54 p.m.
NED2 Entity disambiguation (via description) batch_69a4a7e66ba881908413b647eee4f0da completed March 1, 2026, 8:56 p.m.
Created at: Feb. 28, 2026, 1:12 p.m.