Triple

T9546863
Position Surface form Disambiguated ID Type / Status
Subject Thomas Beall Davis E230312 entity
Predicate givenName P17 FINISHED
Object Thomas
Thomas is a masculine given name of Aramaic origin, widely used in English-speaking countries and historically associated with Christian tradition.
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: [Thomas Beall Davis, givenName, Thomas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas
Context triple: [Thomas Beall Davis, givenName, 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: [Thomas Beall Davis, givenName, Thomas]
Generated description
Thomas is a masculine given name of Aramaic origin, widely used in English-speaking countries and historically associated with Christian tradition.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Thomas
Target entity description: Thomas is a masculine given name of Aramaic origin, widely used in English-speaking countries and historically associated with Christian tradition.
  • 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 first name of English actor Tom Sturridge, known for his work in film, television, and theatre.
  • 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_69ca847c70b8819088a0a0bad64a50d6 completed March 30, 2026, 2:11 p.m.
NER Named-entity recognition batch_69cd9902fca081909125660ae6336d3f completed April 1, 2026, 10:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1527c0914819087ffa9d201afdd35 completed April 4, 2026, 6:03 p.m.
NEDg Description generation batch_69d153d59844819086a0f50e6a7624b2 completed April 4, 2026, 6:09 p.m.
NED2 Entity disambiguation (via description) batch_69d1546a503c81908edc9588adabc172 completed April 4, 2026, 6:11 p.m.
Created at: March 30, 2026, 8:02 p.m.