Triple

T2532811
Position Surface form Disambiguated ID Type / Status
Subject Duke of Luxembourg E56199 entity
Predicate heldBy P8 FINISHED
Object Marshal Luxembourg
Marshal Luxembourg was a prominent French military commander and nobleman who served as Duke of Luxembourg during the reign of Louis XIV.
 E275050 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 Luxembourg | Statement: [Duke of Luxembourg, heldBy, Marshal Luxembourg]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Marshal Luxembourg
Context triple: [Duke of Luxembourg, heldBy, Marshal Luxembourg]
  • A. Helmuth
    Helmuth is a masculine given name of German origin, historically borne by several notable military and political figures.
  • B. Lützow
    Lützow was a German heavy cruiser (originally the pocket battleship Deutschland) that served in the Kriegsmarine during World War II.
  • C. Sperrle
    Sperrle is a German surname most notably borne by Hugo Sperrle, a senior Luftwaffe field marshal during World War II.
  • D. Catroux
    Catroux is a French surname most notably borne by Georges Catroux, a prominent French general and diplomat of the 20th century.
  • E. Meesseman
    Meesseman is the surname of Belgian professional basketball star Emma Meesseman, known for her success in European leagues and the WNBA.
  • 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 Luxembourg
Triple: [Duke of Luxembourg, heldBy, Marshal Luxembourg]
Generated description
Marshal Luxembourg was a prominent French military commander and nobleman who served as Duke of Luxembourg during the reign of Louis XIV.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Marshal Luxembourg
Target entity description: Marshal Luxembourg was a prominent French military commander and nobleman who served as Duke of Luxembourg during the reign of Louis XIV.
  • A. Helmuth
    Helmuth is a masculine given name of German origin, historically borne by several notable military and political figures.
  • B. Lützow
    Lützow was a German heavy cruiser (originally the pocket battleship Deutschland) that served in the Kriegsmarine during World War II.
  • C. Sperrle
    Sperrle is a German surname most notably borne by Hugo Sperrle, a senior Luftwaffe field marshal during World War II.
  • D. Catroux
    Catroux is a French surname most notably borne by Georges Catroux, a prominent French general and diplomat of the 20th century.
  • E. Meesseman
    Meesseman is the surname of Belgian professional basketball star Emma Meesseman, known for her success in European leagues and the WNBA.
  • 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_69ab4a49b6508190bc467fbef4bac334 completed March 6, 2026, 9:42 p.m.
NER Named-entity recognition batch_69abd279cf108190b03fb6e0265f39d9 completed March 7, 2026, 7:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69af2bb9c37081909128d7a227651c8b completed March 9, 2026, 8:21 p.m.
NEDg Description generation batch_69af4fedb0a48190a9d9da8eeebfe074 completed March 9, 2026, 10:55 p.m.
NED2 Entity disambiguation (via description) batch_69af50551fd88190829d20ab2be426d4 completed March 9, 2026, 10:57 p.m.
Created at: March 6, 2026, 9:47 p.m.