Triple

T11593998
Position Surface form Disambiguated ID Type / Status
Subject Louis Thomas Hardy E274953 entity
Predicate middleName P143 FINISHED
Object Thomas
Thomas is a common masculine given name of Aramaic origin, widely used across 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: [Louis Thomas Hardy, middleName, Thomas]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Thomas
Context triple: [Louis Thomas Hardy, middleName, Thomas]
  • A. John
    John is the husband of Martha Rainsborough.
  • B. John
    John is the first name of Pete Ricketts, an American businessman and politician who has served as governor of Nebraska.
  • C. John
    John Seigenthaler was an American journalist, editor, and civil rights advocate best known for his long tenure at The Tennessean and his work promoting First Amendment rights.
  • 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 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: [Louis Thomas Hardy, middleName, Thomas]
Generated description
Thomas is a common masculine given name of Aramaic origin, widely used across many cultures and languages.
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 across 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 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 first name of English actor Tom Sturridge, known for his work in film, television, and theatre.
  • E. 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.
  • 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_69d6aae6b14c81908dc5a74bad7591f9 completed April 8, 2026, 7:22 p.m.
NER Named-entity recognition batch_69d8946594348190935106132fd18028 completed April 10, 2026, 6:10 a.m.
NED1 Entity disambiguation (via context triple) batch_69e713bcc0048190bec14ac4ab84d51d completed April 21, 2026, 6:05 a.m.
NEDg Description generation batch_69e720fc0f38819083bd15169f2ce4bb completed April 21, 2026, 7:02 a.m.
NED2 Entity disambiguation (via description) batch_69e72353c19c8190b7a579e9af823872 completed April 21, 2026, 7:12 a.m.
Created at: April 8, 2026, 9:38 p.m.