Triple

T88845
Position Surface form Disambiguated ID Type / Status
Subject Thomas Alva Edison E1785 entity
Predicate middleName P143 FINISHED
Object Alva
Alva is the middle name of the famed American inventor Thomas Edison, often used as part of his full name, Thomas Alva Edison.
 E21163 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: Alva | Statement: [Thomas Alva Edison, middleName, Alva]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Alva
Context triple: [Thomas Alva Edison, middleName, Alva]
  • A. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • B. Catharina Bolnes
    Catharina Bolnes was the wife of Dutch painter Johannes Vermeer and the mother of his many children, known primarily through her connection to the artist’s life and estate.
  • C. Angela
    Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • 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: Alva
Triple: [Thomas Alva Edison, middleName, Alva]
Generated description
Alva is the middle name of the famed American inventor Thomas Edison, often used as part of his full name, Thomas Alva Edison.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Alva
Target entity description: Alva is the middle name of the famed American inventor Thomas Edison, often used as part of his full name, Thomas Alva Edison.
  • A. Anna
    Anna is the given first name of Eleanor Roosevelt, the influential former First Lady of the United States and human rights advocate.
  • B. Catharina Bolnes
    Catharina Bolnes was the wife of Dutch painter Johannes Vermeer and the mother of his many children, known primarily through her connection to the artist’s life and estate.
  • C. Angela
    Angela is the given name of Angela Merkel, the long-serving former Chancellor of Germany and a prominent European political leader.
  • D. Barbara
    Barbara is a feminine given name of Greek origin that has been widely used in many cultures and languages.
  • E. Theodor
    Theodor "Ted" Nelson is an American pioneer of information technology best known for coining the term "hypertext" and envisioning global hyperlinked document systems.
  • 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_69a24d1a97dc819094e6c021fe9b05a7 completed Feb. 28, 2026, 2:04 a.m.
NER Named-entity recognition batch_69a24f6997c081908b202f937eb2b14f completed Feb. 28, 2026, 2:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69a2e3bbb3508190870d3291a836923e completed Feb. 28, 2026, 12:46 p.m.
NEDg Description generation batch_69a2e41986ec819089095e6843907cd4 completed Feb. 28, 2026, 12:48 p.m.
NED2 Entity disambiguation (via description) batch_69a2e4c29a208190afb2247950c86c59 completed Feb. 28, 2026, 12:51 p.m.
Created at: Feb. 28, 2026, 2:07 a.m.