Triple

T15998317
Position Surface form Disambiguated ID Type / Status
Subject Matt Lanter E388031 entity
Predicate givenName P17 FINISHED
Object Matthew
Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and borne by numerous notable figures in entertainment, sports, and history.
E556162 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: Matthew | Statement: [Matt Lanter, givenName, Matthew]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Matthew
Context triple: [Matt Lanter, givenName, Matthew]
  • A. John
    John is traditionally regarded as the author of the New Testament’s Book of Revelation, a prophetic and apocalyptic text in Christian scripture.
  • B. John
    John is the given name of the late American comedian and actor John Belushi, famed for his work on "Saturday Night Live" and in films like "Animal House" and "The Blues Brothers."
  • C. John
    John is the given name of John A. Macdonald, the first prime minister of Canada and a key figure in the country's Confederation.
  • D. John
    John is the given name of John M. Grunsfeld, an American physicist, former NASA astronaut, and leader in space science and exploration.
  • E. John
    John II Casimir Vasa was a 17th-century King of Poland and Grand Duke of Lithuania from the Swedish House of Vasa.
  • 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: Matthew
Triple: [Matt Lanter, givenName, Matthew]
Generated description
Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and borne by numerous notable figures in entertainment, sports, and history.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Matthew
Target entity description: Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and borne by numerous notable figures in entertainment, sports, and history.
  • A. Matthew chosen
    Matthew is a masculine given name of Hebrew origin, commonly used in English-speaking countries and meaning "gift of God."
  • B. Matthew
    Matthew is the given name of Matt Le Tissier, the renowned former Southampton and England footballer known for his exceptional skill and loyalty to a single club.
  • C. Matthew
    Matthew is the given name of Sir Matt Busby, the legendary Scottish football manager best known for his long and successful tenure at Manchester United.
  • D. Matthew
    Matthew is the given first name of American actor Ryan Phillippe, known for films like "Cruel Intentions" and "Crash."
  • E. Matthew
    Matthew is the given name of the pioneering British Egyptologist and archaeologist Flinders Petrie, renowned for developing systematic excavation and seriation methods.
  • 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_69d86daa562c81908aacc179c0fe8fb5 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e157893ebc8190acb75ee05e450fae completed April 16, 2026, 9:41 p.m.
NED1 Entity disambiguation (via context triple) batch_69ffc3d79dec8190b02e003f93e5dad6 completed May 9, 2026, 11:31 p.m.
NEDg Description generation batch_69ffc50688ac8190b0911ce889254af3 completed May 9, 2026, 11:36 p.m.
NED2 Entity disambiguation (via description) batch_69ffc5902904819097a2c5efbde55882 completed May 9, 2026, 11:38 p.m.
Created at: April 10, 2026, 4:55 a.m.