Triple

T12166874
Position Surface form Disambiguated ID Type / Status
Subject Szymon E289855 entity
Predicate isCognateWith P2527 FINISHED
Object Simon
Simon is a masculine given name of Hebrew origin, widely used in many languages and cultures.
E449149 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: Simon | Statement: [Szymon, isCognateWith, Simon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Simon
Context triple: [Szymon, isCognateWith, Simon]
  • A. Simon
    Simon is the given name of Simon Bolivar Buckner Jr., a U.S. Army lieutenant general who was killed in action while commanding forces during the Battle of Okinawa in World War II.
  • B. Simon
    Simon is a common surname of English and Jewish origin borne by numerous notable individuals across politics, business, arts, and sciences.
  • C. Simon
    Simon is a sleazy used-car salesman and comic-relief character in the action-comedy film "True Lies," who pretends to be a secret agent to seduce women.
  • D. Simon
    Simon is the young, initially timid but ultimately heroic protagonist of the anime series Tengen Toppa Gurren Lagann, known for piloting powerful mecha and embodying themes of growth and determination.
  • E. Simon
    Simon is a 1980 satirical science-fiction comedy film written and directed by Marshall Brickman, centered on a psychology professor who is tricked into believing he is an alien.
  • 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: Simon
Triple: [Szymon, isCognateWith, Simon]
Generated description
Simon is a masculine given name of Hebrew origin, widely used in many languages and cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Simon
Target entity description: Simon is a masculine given name of Hebrew origin, widely used in many languages and cultures.
  • A. Simon chosen
    Simon is a common masculine given name of Hebrew origin, widely used in many cultures and languages.
  • B. Simon
    Simon is a common surname of English and Jewish origin borne by numerous notable individuals across politics, business, arts, and sciences.
  • C. Simon
    Simon is the given name of Simon Bolivar Buckner Jr., a U.S. Army lieutenant general who was killed in action while commanding forces during the Battle of Okinawa in World War II.
  • D. Simon
    Simon is the young, initially timid but ultimately heroic protagonist of the anime series Tengen Toppa Gurren Lagann, known for piloting powerful mecha and embodying themes of growth and determination.
  • E. Simon
    Simon is the central character in Ang Lee's 1993 film "The Wedding Banquet," a Taiwanese American man who enters a sham marriage to appease his traditional parents while secretly living with his male partner in New York.
  • 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_69d6ab4d6c00819095a9a7c35de83cfb completed April 8, 2026, 7:23 p.m.
NER Named-entity recognition batch_69d915d85c088190a74fb7590877659b completed April 10, 2026, 3:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69f60a85e42481908c5517a24f7688e0 completed May 2, 2026, 2:30 p.m.
NEDg Description generation batch_69f60bdb39f48190ad6bc51db6c34163 completed May 2, 2026, 2:36 p.m.
NED2 Entity disambiguation (via description) batch_69f60d4c30448190874f253b864ef61e completed May 2, 2026, 2:42 p.m.
Created at: April 8, 2026, 9:50 p.m.