Triple

T4066239
Position Surface form Disambiguated ID Type / Status
Subject Thomson E86329 entity
Predicate derivedFromGivenName P17 FINISHED
Object Thomas
Thomas is a common masculine given name of Aramaic origin, widely used in many cultures 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: [Thomson, derivedFromGivenName, Thomas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas
Context triple: [Thomson, derivedFromGivenName, Thomas]
  • A. Thomas
    Thomas is the middle name of Edward Thomas Wailes, an American diplomat.
  • B. Thomas
    Thomas is the given first name of the renowned American playwright Tennessee Williams, known for works such as "A Streetcar Named Desire" and "Cat on a Hot Tin Roof."
  • C. Thomas
    Thomas is the given name of English actor Tom Courtenay, a prominent figure in British cinema and theatre since the 1960s.
  • D. Thomas
    Thomas is the middle name of William T. Sampson, a notable U.S. Navy admiral from the Spanish–American War era.
  • E. Thomas
    Thomas is the given first name of American astronaut Ken Mattingly, known for his role in the Apollo and Space Shuttle programs.
  • 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: [Thomson, derivedFromGivenName, Thomas]
Generated description
Thomas is a common masculine given name of Aramaic origin, widely used in many cultures 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 common masculine given name of Aramaic origin, widely used in many cultures 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 full given name of Tom Brady, the legendary NFL quarterback widely regarded as one of the greatest players in American football history.
  • 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_69aed93c69208190a4efac0efe3cd69b completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefbf58d9c8190936e453b0d397cb0 completed March 9, 2026, 4:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69b56b55b5388190a90551c43388f3fc completed March 14, 2026, 2:06 p.m.
NEDg Description generation batch_69b56bdcfa94819096df212e6e99937e completed March 14, 2026, 2:08 p.m.
NED2 Entity disambiguation (via description) batch_69b56c5e73e88190896176180a9e58cd completed March 14, 2026, 2:10 p.m.
Created at: March 9, 2026, 3:38 p.m.