Triple

T11162149
Position Surface form Disambiguated ID Type / Status
Subject Thomas Lasorda E264061 entity
Predicate hasGivenName P17 FINISHED
Object Thomas
Thomas is a masculine given name of Aramaic origin meaning "twin," widely used in many cultures and languages.
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 Lasorda, hasGivenName, Thomas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas
Context triple: [Thomas Lasorda, hasGivenName, Thomas]
  • A. John
    John IV of Portugal was a 17th-century Portuguese king who restored the country's independence from Spain and founded the Braganza dynasty.
  • B. John
    John is the given name of John Henry Patterson, an American industrialist and founder of the National Cash Register Company.
  • C. John
    John is the first name of J. Michael Luttig, a prominent American conservative jurist and former federal appellate judge.
  • D. John
    John was a historical Prince of Asturias, the traditional title for the heir apparent to the Spanish throne.
  • E. John
    John is the given name of John Copley, 1st Baron Lyndhurst, a prominent 19th-century British lawyer and politician who served three times as Lord Chancellor.
  • 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 Lasorda, hasGivenName, Thomas]
Generated description
Thomas is a masculine given name of Aramaic origin meaning "twin," widely used in many cultures and languages.
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 meaning "twin," widely used in many cultures and languages.
  • 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 Malthus, the influential English economist and demographer known for his theories on population growth and resource limits.
  • D. 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."
  • 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_69d6aa9ccddc8190868998c8b7beb060 completed April 8, 2026, 7:21 p.m.
NER Named-entity recognition batch_69d7e8832fe88190a74d81f9ed547baa completed April 9, 2026, 5:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69e4aced563c8190a56ab5ff0618d21f completed April 19, 2026, 10:22 a.m.
NEDg Description generation batch_69e4afa531e0819097587675198bb8a1 completed April 19, 2026, 10:34 a.m.
NED2 Entity disambiguation (via description) batch_69e4b1f68c8c819096d58ac02d76ec0d completed April 19, 2026, 10:44 a.m.
Created at: April 8, 2026, 9:29 p.m.