Triple

T15155007
Position Surface form Disambiguated ID Type / Status
Subject John Henry Poynting E362047 entity
Predicate givenName P17 FINISHED
Object John
John is the given name of John Henry Poynting, a prominent English physicist known for formulating the Poynting vector in electromagnetism.
E1141013 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: John | Statement: [John Henry Poynting, givenName, John]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: John
Context triple: [John Henry Poynting, givenName, John]
  • A. John
    John is the given first name of Johnny Kilbane, an American featherweight boxing champion from the early 20th century.
  • B. John
    John Ross is a personal name shared by various notable individuals across history, including leaders, politicians, and public figures.
  • C. John
    John is the given name of John Adams, the second president of the United States and a prominent Founding Father.
  • D. John
    John is the given name of John C. Sheehan, an American organic chemist renowned for achieving the first complete laboratory synthesis of penicillin.
  • E. John
    John is the given name of John Bowen, a British novelist and playwright known for his crime and speculative fiction.
  • 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: John
Triple: [John Henry Poynting, givenName, John]
Generated description
John is the given name of John Henry Poynting, a prominent English physicist known for formulating the Poynting vector in electromagnetism.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: John
Target entity description: John is the given name of John Henry Poynting, a prominent English physicist known for formulating the Poynting vector in electromagnetism.
  • A. John
    John is the given name of John Cockcroft, a pioneering British physicist and Nobel laureate known for his work on nuclear physics and particle acceleration.
  • B. John
    John is the given name of the British physicist J. J. Thomson, who is best known for discovering the electron and proposing the plum pudding model of the atom.
  • C. John
    John is the given name of Sir John Lennard-Jones, a pioneering British theoretical chemist known for his work on intermolecular forces and the Lennard-Jones potential.
  • D. John
    John is the given name of John Clive Ward, a notable British-Australian theoretical physicist known for his contributions to quantum electrodynamics and the Ward–Takahashi identity.
  • E. John
    John is the given name of John Hopfield, an American physicist and neuroscientist known for pioneering work on Hopfield networks in artificial intelligence.
  • 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_69d85a0759908190b8a051d2e2a1cbe6 completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0060b0cd08190afad14cffcc7d93f completed April 15, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69fec88419108190860319a9bcab1eef completed May 9, 2026, 5:39 a.m.
NEDg Description generation batch_69fec93f7c9c8190b9d1722180517d7c completed May 9, 2026, 5:42 a.m.
NED2 Entity disambiguation (via description) batch_69fec9c518a08190b0ae7adad43a7f2c completed May 9, 2026, 5:44 a.m.
Created at: April 10, 2026, 3:08 a.m.