Triple

T10921627
Position Surface form Disambiguated ID Type / Status
Subject Mark Obama Ndesandjo E257959 entity
Predicate givenName P17 FINISHED
Object Mark
Mark is a common masculine given name used in many English-speaking and other cultures, derived from the Latin name Marcus.
 E161211 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: Mark | Statement: [Mark Obama Ndesandjo, givenName, Mark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Mark
Context triple: [Mark Obama Ndesandjo, givenName, Mark]
  • A. Mark
    The Mark was the basic unit of currency used in Germany during various historical periods, including the era of the Papiermark.
  • B. Mark
    Mark is a quirky, music-obsessed employee at the independent record store in the 1995 cult film "Empire Records," known for his goofy charm and laid-back attitude.
  • C. Mark
    Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
  • D. Mark
    Mark is a punctuation symbol used in writing systems, including those that employ the Cyrillic Extended-B Unicode block.
  • E. Mark
    Mark is one of the four canonical Gospels in the New Testament, traditionally attributed to John Mark and known for its concise, fast-paced account of the life, ministry, death, and resurrection of Jesus Christ.
  • 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: Mark
Triple: [Mark Obama Ndesandjo, givenName, Mark]
Generated description
Mark is a common masculine given name used in many English-speaking and other cultures, derived from the Latin name Marcus.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Mark
Target entity description: Mark is a common masculine given name used in many English-speaking and other cultures, derived from the Latin name Marcus.
  • A. Mark chosen
    Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
  • B. Mark
    Mark is the given name of Mark Zuckerberg, the American technology entrepreneur and co-founder of Facebook.
  • C. Mark
    Mark is a punctuation symbol used in writing systems, including those that employ the Cyrillic Extended-B Unicode block.
  • D. Mark
    Mark is one of the four canonical Gospels in the New Testament, traditionally attributed to John Mark and known for its concise, fast-paced account of the life, ministry, death, and resurrection of Jesus Christ.
  • E. Mark
    The Mark was the basic unit of currency used in Germany during various historical periods, including the era of the Papiermark.
  • 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_69d6aa864ed88190818280ab6791d065 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d77082a1488190850a4409339c3e1e completed April 9, 2026, 9:25 a.m.
NED1 Entity disambiguation (via context triple) batch_69e2170bb97c81908e8d209ddb630601 completed April 17, 2026, 11:18 a.m.
NEDg Description generation batch_69e21d8a2e6881909b33cbe4ab919315 completed April 17, 2026, 11:46 a.m.
NED2 Entity disambiguation (via description) batch_69e21eaa1e9881909f3b276e0ff0c511 completed April 17, 2026, 11:51 a.m.
Created at: April 8, 2026, 9:22 p.m.