Triple

T9582970
Position Surface form Disambiguated ID Type / Status
Subject Tomasz E231217 entity
Predicate cognateOf P8954 FINISHED
Object Thomas
Thomas is a widely used male given name of Aramaic origin, common in many European languages and associated with the biblical apostle Saint Thomas.
E67625 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: Thomas | Statement: [Tomasz, cognateOf, Thomas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas
Context triple: [Tomasz, cognateOf, Thomas]
  • A. John
    John is the husband of Martha Rainsborough.
  • B. John
    John W. Tukey was an influential American mathematician and statistician known for pioneering exploratory data analysis and coining the term "bit."
  • C. John
    John is the given name of Colonel John Quincy, an American military officer and politician after whom John Quincy Adams was named.
  • D. John
    John is the given name of John Vlissides, a software engineer best known as one of the “Gang of Four” authors of the influential book *Design Patterns: Elements of Reusable Object-Oriented Software*.
  • E. John
    John was a historical Prince of Asturias, the traditional title for the heir apparent to the Spanish throne.
  • 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: Thomas
Triple: [Tomasz, cognateOf, Thomas]
Generated description
Thomas is a widely used male given name of Aramaic origin, common in many European languages and associated with the biblical apostle Saint Thomas.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Thomas
Target entity description: Thomas is a widely used male given name of Aramaic origin, common in many European languages and associated with the biblical apostle Saint Thomas.
  • A. Thomas chosen
    Thomas is a common masculine given name of Aramaic origin, widely used in English-speaking and many other cultures.
  • B. Thomas
    Thomas is a common surname of English and Welsh origin, derived from the given name Thomas and borne by numerous notable individuals worldwide.
  • C. Thomas
    Thomas is the given name of Thomas Paine, the influential 18th-century political philosopher and writer known for works like "Common Sense" and "The Rights of Man."
  • D. Thomas
    Thomas is the given name of Thomas Malthus, the influential English economist and demographer known for his theories on population growth and resource limits.
  • E. Thomas
    Thomas is the given name of Thomas Cranmer, the 16th-century Archbishop of Canterbury and a leading figure in the English Reformation.
  • F. None of above.

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_69ca848161688190a68d514a0a9d5129 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd99cebaf081908033536a53fbf668 completed April 1, 2026, 10:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1821797f88190b427ad1fcfc73757 completed April 4, 2026, 9:26 p.m.
NEDg Description generation batch_69d182cb723881909f29b6a875e99da1 completed April 4, 2026, 9:29 p.m.
NED2 Entity disambiguation (via description) batch_69d1833692788190990ad426b6502488 completed April 4, 2026, 9:31 p.m.
Created at: March 30, 2026, 8:05 p.m.