Triple

T439256
Position Surface form Disambiguated ID Type / Status
Subject Nine Years' War E10076 entity
Predicate hasCommander P1197 FINISHED
Object Marshal Catinat
Marshal Catinat was a prominent 17th-century French general and Marshal of France, noted for his disciplined leadership and key victories under Louis XIV.
 E55387 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: Marshal Catinat | Statement: [Nine Years' War, hasCommander, Marshal Catinat]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marshal Catinat
Context triple: [Nine Years' War, hasCommander, Marshal Catinat]
  • A. Lennox Cato
    Lennox Cato is a British antiques dealer and television expert best known for his appearances on the BBC’s "Antiques Roadshow."
  • B. Castera Bazile
    Castera Bazile was a Haitian artist known for his religious murals and contributions to the visual arts of Haiti.
  • C. Fidelis
    Fidelis is a Latin word meaning "faithful" or "loyal," commonly used in mottos and phrases to express steadfast allegiance and reliability.
  • D. Roland Caulder
    Roland Caulder is an actor known for his role in the film "The Iron Mask."
  • E. Philip Sabes
    Philip Sabes is a neuroscientist and entrepreneur known for his work on brain–computer interfaces and for co-founding the neurotechnology company Neuralink.
  • 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: Marshal Catinat
Triple: [Nine Years' War, hasCommander, Marshal Catinat]
Generated description
Marshal Catinat was a prominent 17th-century French general and Marshal of France, noted for his disciplined leadership and key victories under Louis XIV.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marshal Catinat
Target entity description: Marshal Catinat was a prominent 17th-century French general and Marshal of France, noted for his disciplined leadership and key victories under Louis XIV.
  • A. Lennox Cato
    Lennox Cato is a British antiques dealer and television expert best known for his appearances on the BBC’s "Antiques Roadshow."
  • B. Castera Bazile
    Castera Bazile was a Haitian artist known for his religious murals and contributions to the visual arts of Haiti.
  • C. Fidelis
    Fidelis is a Latin word meaning "faithful" or "loyal," commonly used in mottos and phrases to express steadfast allegiance and reliability.
  • D. Roland Caulder
    Roland Caulder is an actor known for his role in the film "The Iron Mask."
  • E. Philip Sabes
    Philip Sabes is a neuroscientist and entrepreneur known for his work on brain–computer interfaces and for co-founding the neurotechnology company Neuralink.
  • 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_69a2e8465ef481909655c681b01e2986 completed Feb. 28, 2026, 1:06 p.m.
NER Named-entity recognition batch_69a2ef283be881909444aaf257451747 completed Feb. 28, 2026, 1:35 p.m.
NED1 Entity disambiguation (via context triple) batch_69a4366eb0588190beb5c43828f8ca45 completed March 1, 2026, 12:51 p.m.
NEDg Description generation batch_69a436c6f1a0819086f8d8f12bc82e87 completed March 1, 2026, 12:53 p.m.
NED2 Entity disambiguation (via description) batch_69a4379eee248190a417b81afbb403ae completed March 1, 2026, 12:57 p.m.
Created at: Feb. 28, 2026, 1:11 p.m.