Triple

T974619
Position Surface form Disambiguated ID Type / Status
Subject Michael E21023 entity
Predicate hasVariant P455 FINISHED
Object Michaël
Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
 E114788 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: Michaël | Statement: [Michael, hasVariant, Michaël]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Michaël
Context triple: [Michael, hasVariant, Michaël]
  • A. Michel
    Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
  • B. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • C. Myles
    Myles is a masculine given name of English origin, historically associated with figures such as Mayflower military leader Myles Standish.
  • D. Jeffrey
    Jeffrey is a masculine given name of Germanic origin, commonly used in English-speaking countries.
  • E. Micah
    Micah is a minor biblical figure in the Book of Judges known for establishing a private shrine and hiring a Levite as his personal priest, illustrating the religious disorder of the period.
  • 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: Michaël
Triple: [Michael, hasVariant, Michaël]
Generated description
Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Michaël
Target entity description: Michaël is a given name, typically a French or Dutch variant of the name Michael, used for males in various European countries.
  • A. Michel
    Michel is the birth name of the acclaimed Egyptian actor Omar Sharif, renowned for his roles in classic films such as "Lawrence of Arabia" and "Doctor Zhivago."
  • B. Mick
    Mick is the commonly used nickname of American politician and former White House Chief of Staff Mick Mulvaney.
  • C. Myles
    Myles is a masculine given name of English origin, historically associated with figures such as Mayflower military leader Myles Standish.
  • D. Jeffrey
    Jeffrey is a masculine given name of Germanic origin, commonly used in English-speaking countries.
  • E. Micah
    Micah is a minor biblical figure in the Book of Judges known for establishing a private shrine and hiring a Levite as his personal priest, illustrating the religious disorder of the period.
  • F. None of above. chosen

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_69a493c2b62c8190b616351789ec47f8 completed March 1, 2026, 7:30 p.m.
NER Named-entity recognition batch_69a4b460a5c0819087b03dfb8a3af2c2 completed March 1, 2026, 9:49 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac170c0fdc8190b904ca5737764f5a completed March 7, 2026, 12:16 p.m.
NEDg Description generation batch_69ac17c2e6f48190be6fce7f279957c4 completed March 7, 2026, 12:19 p.m.
NED2 Entity disambiguation (via description) batch_69ac1844acec81909859605d2421a588 completed March 7, 2026, 12:21 p.m.
Created at: March 1, 2026, 7:40 p.m.