Triple

T14287882
Position Surface form Disambiguated ID Type / Status
Subject Mark Mancina E354225 entity
Predicate givenName P17 FINISHED
Object Mark
Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
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 Mancina, givenName, Mark]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mark
Context triple: [Mark Mancina, 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 refers to the historic Margraviate of Brandenburg, a key principality of the Holy Roman Empire that later formed the core territory of Brandenburg-Prussia.
  • D. Mark
    Mark is a river in the southern Netherlands and northern Belgium that flows through the province of North Brabant before joining the Dintel.
  • E. Mark
    Mark is a punctuation symbol used in writing systems, including those that employ the Cyrillic Extended-B Unicode block.
  • 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 Mancina, givenName, Mark]
Generated description
Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Mark
Target entity description: Mark is a common masculine given name of Latin origin, derived from Marcus and historically associated with figures such as the evangelist Saint Mark.
  • 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 the given name of filmmaker Mark Neveldine, known for co-directing high-energy action films like the "Crank" series.
  • 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
    Mark is the real first name of The Undertaker, the legendary professional wrestler best known for his long-running career in WWE.
  • 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_69d8278e17088190b328c5a9d4be74ff completed April 9, 2026, 10:26 p.m.
NER Named-entity recognition batch_69de698023288190b1d705235c2b2ca3 completed April 14, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69fd3d1e14d4819091c381f96c43c58b completed May 8, 2026, 1:32 a.m.
NEDg Description generation batch_69fd3e6dde6081908a37817e4dd22ecd completed May 8, 2026, 1:37 a.m.
NED2 Entity disambiguation (via description) batch_69fd3f42ccdc81908399d8f9a2f0da31 completed May 8, 2026, 1:41 a.m.
Created at: April 10, 2026, 1:11 a.m.