Triple

T6256099
Position Surface form Disambiguated ID Type / Status
Subject Aimé Millet E140168 entity
Predicate familyName P18 FINISHED
Object Millet
Millet is a common French surname borne by several notable figures, including artists and sculptors.
 E579319 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: Millet | Statement: [Aimé Millet, familyName, Millet]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Millet
Context triple: [Aimé Millet, familyName, Millet]
  • A. Emmer
    Emmer is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia before joining the Weser.
  • B. Barley
    Barley is a small rural village in the North Hertfordshire district of England, known for its historic buildings and traditional English countryside setting.
  • C. Dinkel
    Dinkel is a small river in the eastern Netherlands and western Germany, known for flowing through the Twente region and its relatively unspoiled natural landscapes.
  • D. Farino
    Farino is a small rural commune in the South Province of New Caledonia, known for its lush forests and eco-tourism activities.
  • E. Kasha
    Kasha is a feminine given name used in various cultures, often as a diminutive or variant of names like Katarzyna or Kasia.
  • 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: Millet
Triple: [Aimé Millet, familyName, Millet]
Generated description
Millet is a common French surname borne by several notable figures, including artists and sculptors.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Millet
Target entity description: Millet is a common French surname borne by several notable figures, including artists and sculptors.
  • A. Emmer
    Emmer is a river in northwestern Germany that flows through Lower Saxony and North Rhine-Westphalia before joining the Weser.
  • B. Barley
    Barley is a small rural village in the North Hertfordshire district of England, known for its historic buildings and traditional English countryside setting.
  • C. Dinkel
    Dinkel is a small river in the eastern Netherlands and western Germany, known for flowing through the Twente region and its relatively unspoiled natural landscapes.
  • D. Farino
    Farino is a small rural commune in the South Province of New Caledonia, known for its lush forests and eco-tourism activities.
  • E. Kasha
    Kasha is a feminine given name used in various cultures, often as a diminutive or variant of names like Katarzyna or Kasia.
  • 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_69c008b4858c819095b0199114a9a87b completed March 22, 2026, 3:20 p.m.
NER Named-entity recognition batch_69c063653910819095f1dc3b90ce77db completed March 22, 2026, 9:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69c244379f308190b73fe7ed4ed678e9 completed March 24, 2026, 7:58 a.m.
NEDg Description generation batch_69c246309e5081908cd00cdf15f545d3 completed March 24, 2026, 8:07 a.m.
NED2 Entity disambiguation (via description) batch_69c246cd73608190a76e1d99da153338 completed March 24, 2026, 8:09 a.m.
Created at: March 22, 2026, 4:24 p.m.