Triple

T273741
Position Surface form Disambiguated ID Type / Status
Subject Miller E5201 entity
Predicate hasVariant P455 FINISHED
Object Müller
Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
 E35404 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: Müller | Statement: [Miller, hasVariant, Müller]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Müller
Context triple: [Miller, hasVariant, Müller]
  • A. Morgenstern
    Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
  • B. Hermann
    Hermann Minkowski was a German mathematician best known for developing the geometric formulation of special relativity using four-dimensional spacetime.
  • C. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • D. Erwin
    Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
  • E. Ulrich Merkel
    Ulrich Merkel is a German physicist best known as the first husband of former German chancellor Angela Merkel.
  • 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: Müller
Triple: [Miller, hasVariant, Müller]
Generated description
Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Müller
Target entity description: Müller is a common German surname, equivalent to "Miller" in English, historically associated with the occupation of operating a mill.
  • A. Morgenstern
    Morgenstern is a German surname borne by various notable figures in fields such as economics, literature, and the arts.
  • B. Hermann
    Hermann Minkowski was a German mathematician best known for developing the geometric formulation of special relativity using four-dimensional spacetime.
  • C. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • D. Erwin
    Erwin is a masculine given name of German origin, historically associated with figures such as the World War II field marshal Erwin Rommel.
  • E. Ulrich Merkel
    Ulrich Merkel is a German physicist best known as the first husband of former German chancellor Angela Merkel.
  • 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_69a257e6c8788190987dfe705ca2912a completed Feb. 28, 2026, 2:50 a.m.
NER Named-entity recognition batch_69a25dd0a99c819089968a5400c58c5f completed Feb. 28, 2026, 3:15 a.m.
NED1 Entity disambiguation (via context triple) batch_69a38f537f1c8190a59ac4669498a3fc completed March 1, 2026, 12:58 a.m.
NEDg Description generation batch_69a38fbfac808190b2b551dcbfe6faff completed March 1, 2026, 1 a.m.
NED2 Entity disambiguation (via description) batch_69a3903779e88190a00c44a522e82022 completed March 1, 2026, 1:02 a.m.
Created at: Feb. 28, 2026, 2:59 a.m.